BrainTube — An index of ideas

Day 23 of great biology papers.

Fri, 11 Sep 2026 23:28:02 GMT

Day 23 of great biology papers. "The genetics of Caenorhabditis elegans," by Sydney Brenner (1974). 7 years, 300 worm mutants, 100 genes. This paper kicked off Brenner's long-term aim: To map how every gene affects the "development and functioning" of a multicellular organism. ** During a four-year span, from 1961-1965, Sydney Brenner was involved in some of molecular biology's most seminal discoveries. He helped isolate messenger RNA for the first time (1961), establish the "triplet" genetic code with Francis Crick (1961), and identify stop codons for polypeptide "chain termination" (1965). And then, he pivoted. In 1963, he wrote a letter to a friend, stating that he wanted to "tame a small metazoan organism to study development directly." In other words, to link chemical changes in genes to visible changes in behavior.(https://t.co/IGmWGAxbir) The nematode, C. elegans, seemed perfect for the job. Brenner obtained some worms in 1963. They are easy to grow on standard agar plates, produce many offspring (quickly), and have far fewer neurons than the fruit flies that T.H. Morgan had studied at Caltech in the early 20th century. C. elegans DNA can also be easily mutated using ethyl methanesulfonate, or EMS. (https://t.co/6K9jTULV48) In 1963, Brenner applied for funding from the Medical Research Council at Cambridge. He said he wanted to "identify every cell in the worm and trace lineages," and also to "investigate the constancy of development and study its control by looking for mutants." (https://t.co/IGmWGAxbir) Work began in 1967. For the next seven years, Brenner did painstaking experiments on his worms. In 1974, his single author paper in the journal Genetics explained how to work with nematodes, yes, but also "reported on hundreds of mutants—long worms, rolling worms, dumpy-looking worms, uncoordinated worms, blistered worms, and worms whose heads were notched or bent" that he had made with EMS. Brenner identified 96 genetic loci on all six chromosomes in the worms. Although Brenner did not achieve his first goal — "to identify every cell in the worm and trace lineages" — another group of biologists, also in Cambridge, did so in 1983. (https://t.co/nesUsdEItp) Paper: https://t.co/nIgWRH8M0i (h/t @CarlosSanz22 for the suggestion.)

In 1980, two years before Feynman's famous Caltech lecture on Quantum Computing, a 43-year-old Soviet mathematician named Yuri Manin published a slim 128-page popular-science book called Вычислимое и невычислимое — Computable and Noncomputable — through the Moscow publishing house Sov. Radio. Manin

Fri, 11 Sep 2026 22:58:42 GMT

In 1980, two years before Feynman's famous Caltech lecture on Quantum Computing, a 43-year-old Soviet mathematician named Yuri Manin published a slim 128-page popular-science book called Вычислимое и невычислимое — Computable and Noncomputable — through the Moscow publishing house Sov. Radio. Manin was not a computer scientist. He was already one of the great algebraic geometers of his generation: a Lenin Prize laureate (1967), professor of algebra at Moscow State University, principal researcher at the Steklov Mathematical Institute, the mathematician behind the Gauss–Manin connection and the Mordell conjecture for function fields. He had been forbidden from foreign travel since 1968. The book was written in Russian, never officially translated for nearly thirty years, and its argument about quantum computation took up barely three pages of the introduction... https://t.co/pOZ0460JWB What's striking about Manin's framing — and what got almost entirely lost when the Western quantum computing canon formed around Benioff, Feynman, and Deutsch — is the direction of the argument. Feynman's 1982 case for quantum computers was pragmatic and engineering-flavored: classical machines can't efficiently simulate quantum systems, therefore we should build quantum machines that can. Manin came at it from the opposite end. He looked at molecular biology — at protein synthesis on messenger RNA, at the absurd information density and energetic efficiency with which living cells perform what looks structurally like Turing-machine computation — and concluded that nature had already solved the problem. Classical physics, he argued, simply cannot account for what biology does. The mathematical theory of quantum automata must already be implicit in the substrate of life. Engineering quantum computers wasn't the goal; it was the obvious downstream consequence of taking biology's existence-proof seriously. That places Manin in a different intellectual lineage than the one quantum computing eventually inherited. He was downstream of Schrödinger's What Is Life? (1944) and the broader Soviet tradition of treating life as a physical system whose laws had not yet been written — Vernadsky, Lyapunov, the cybernetics revival under Berg and Glushkov. The West built quantum computing as an engineering discipline of qubits-as-fabricated-systems, and pushed biology off into a separate and often-dismissed sub-field called "quantum biology." Forty-five years later, with the work emerging on microtubules, tryptophan networks, ordered water, and coherent processes in neural lattices, the field is, in a real sense, finally catching up to its own actual origin. The translation below is from pages 13–15 of the introduction. On the inefficiency of computing devices Molecular biology provides examples of the behavior of natural (not human-engineered) systems which we are forced to describe in terms close to those accepted in the theory of discrete automata. The figure below depicts the scheme of protein synthesis on messenger RNA: it closely resembles the depiction of a Turing machine copying information from one tape to another. Classical continuous systems governed by differential equations can imitate discrete automata only when their phase space has an exceptionally complex structure — an abundance of stability regions separated by low energy barriers. Loading a program carves out a sophisticated system of passages through these barriers, predetermining the motion of the phase trajectory through this labyrinth. As a physical system, the computing device must be highly unstable, since an error of a single character in the program generally leads to an entirely different trajectory. Yet the computational process itself must be exceptionally stable — that is, spontaneous errors (transitions of the trajectory across a barrier that should remain closed, as a result of fluctuations) must have very low probability. It is well known that these requirements — combined with slowness of operation and the exponential growth of dissipated energy as complexity increases — erected the barrier that halted the development of mechanical computers. [Citing Poplavsky's 1975 paper on thermodynamic models of information processes:] A genuinely instructive calculation can be found there: the quantum-mechanical description of the methane molecule by the lattice method requires computation at 10⁴² points. If we assume only 10 elementary operations are performed at each point, and suppose all computations are carried out at ultra-low temperature, then even so the calculation of the methane molecule would require expending energy roughly equal to that produced on Earth over a century. On quantum automata It is possible that for a better understanding of such phenomena a mathematical theory of quantum automata is lacking. The mathematical model of such objects must exhibit highly unusual properties compared with deterministic processes. The reason is that the capacity of the quantum state space is dramatically greater: where in the classical case there are N discrete states, in quantum theory — which permits their superposition — the state space lies in Cᴺ. When classical systems are combined, their state-counts N₁ and N₂ simply multiply; in the quantum case one obtains C^(N₁·N₂). These rough estimates show that systems exhibiting quantum behavior are potentially far more complex than their classical counterparts. For example, since the system has no unique decomposition into parts, the state of a quantum automaton may be regarded in many different ways as states of entirely different virtual classical automata. In carrying out such a program, the first difficulty will be finding the right balance between mathematical and physical principles. The quantum automaton must be abstract: its mathematical model should use only the most general quantum principles, without prejudging physical implementations. Then the model of evolution is a unitary rotation in finite-dimensional Hilbert space, and the virtual decomposition into subsystems corresponds to the tensor-product decomposition of that space. Somewhere in this picture the place of interactions — traditionally described by Hermitian operators and probabilities — must still be found. Notes on this translation: The C in "Cᴺ" is the field of complex numbers; Cᴺ is N-dimensional complex Hilbert space. C^(N₁·N₂) reflects the tensor product H₁ ⊗ H₂ — the structure that gives quantum systems their entanglement-driven computational advantage. The Poplavsky reference is to R.P. Poplavsky, "Thermodynamical models of information processing," Uspekhi Fizicheskikh Nauk 115:3 (1975), 465–501.

Lecture 3 | Modern Physics: Quantum Mechanics (Stanford)

Fri, 11 Sep 2026 22:51:21 GMT

Lecture 3 of Leonard Susskind's Modern Physics course concentrating on Quantum Mechanics. Recorded January 28, 2008 at Stanford University. This Stanford Continuing Studies course is the second of a six-quarter sequence of classes exploring the essential theoretical foundations of modern physics. The topics covered in this course focus on quantum mechanics. Leonard Susskind is the Felix Bloch Professor of Physics at Stanford University. Complete playlist for the course: http://youtube.com/view_play_list?p=189C0DCE90CB6D81 Stanford Continuing Studies: http://continuingstudies.stanford.edu/ About Leonard Susskind: http://www.stanford.edu/dept/physics/people/faculty/susskind_leonard.html Stanford University channel on YouTube: http://www.youtube.com/stanford

Lec 08. Architectures: Transformers

Fri, 11 Sep 2026 22:36:41 GMT

MIT 6.7960 Deep Learning, Fall 2024 Instructor: Phillip Isola View the complete course: https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/ YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63URZnh5iqBzDTDYPUTQT-8 This video introduces transformers, focusing on three key ideas: tokens, attention, and positional codes. It also explores how transformers relate to MLPs, GNNs, and CNNs as variations on common principles. License: Creative Commons BY-NC-SA More information at https://ocw.mit.edu/terms More courses at https://ocw.mit.edu Support OCW at http://ow.ly/a1If50zVRlQ We encourage constructive comments and discussion on OCW’s YouTube and other social media channels. Personal attacks, hate speech, trolling, and inappropriate comments are not allowed and may be removed. More details at https://ocw.mit.edu/comments.

5.1 Introduction to Knowledge

Fri, 11 Sep 2026 21:52:41 GMT

A series of lectures delivered by Peter Millican to first-year philosophy students at the University of Oxford. The lectures comprise the 8-week General Philosophy course and were delivered in late 2009.

1. Course introduction

Fri, 11 Sep 2026 21:52:41 GMT

Death (PHIL 176) Professor Kagan introduces the course and the material that will be covered during the semester. He aims to clarify what the class will focus on in particular and which subjects it will steer away from. The emphasis will be placed on philosophical questions that arise when one contemplates the nature of death. The first half of the course will address metaphysical questions while the second half will focus on value theory. 00:00 - Chapter 1. What This Class Is NOT, and What This Class Is 06:38 - Chapter 2. Common Views on Death Are Wrong: How This Class Is Taught 14:20 - Chapter 3. Course Requirements, Materials, and Format 21:03 - Chapter 4. On Grading Thoughts on Death 38:50 - Chapter 5. Student Evaluations and Invitation This course was recorded in Spring 2007.

🚨 Harvard just made its AI course free.

Fri, 11 Sep 2026 21:45:21 GMT

🚨 Harvard just made its AI course free. No tuition. No application. Just world-class AI education. Harvard University just released 6 high-quality lectures on AI + Prompt Engineering— and anyone can access them. If you're serious about AI in 2026, start here: 🎓 Harvard University LECTURES 1. Introduction to Generative AI A step-by-step guide to how GenAI actually works. Link: https://t.co/0XZmwrazHA 2. Prompt Engineering Real tips for improving output quality from any LLM. Link: https://t.co/pqCWQExXyZ 3. Beyond Chatbots: System Prompts, RAG Move past surface use cases into scalable applications. Link: https://t.co/EuUnb8vozU 4. Generative AI in Teaching & Learning How educators can adapt and lead with AI. Playlist: https://t.co/zqSvlbtUli 5. Teaching with AI in the Classroom Frameworks for trainers and educators. Link: https://t.co/8joDmfqgFv 6. The Basics of Generative AI No jargon. Just clarity. Link: https://t.co/bOOutH2VKb BONUS 🟩 CS50x 2025 – Artificial Intelligence Lecture LLMs, neural nets, and real-world use cases Link: https://t.co/KM4s8lG7w0 🟩 CS50 Extension – AI / Prompt Engineering Design prompts that think with you Link: https://t.co/x3dqziTJiI 🟩 GPT-4: How it works + how to build with it Behind the curtain on GPT-4 Video: https://t.co/S4luR33uC2 🟩 LLMs and the End of Programming Why prompting is the new coding Video: https://t.co/VckvhYqxIe --- 💡 REALITY CHECK ✅ Harvard-level AI knowledge is now free ✅ Prompting = career acceleration ✅ 2 lectures can put you ahead of 90% of professionals ✅ The future belongs to people who can “talk to machines” strategically Don’t just consume AI. Learn how to control it. 👇 Which lecture are you starting with? ♻️ Repost to help others learn AI 🔖 Save this (you’ll need it later) 🔔 Follow @AnnuKumari35786 for more AI insights

🚨 Harvard just made its AI course free.

Fri, 11 Sep 2026 21:45:21 GMT

🚨 Harvard just made its AI course free.

Fri, 11 Sep 2026 21:45:21 GMT

🚨 Harvard just made its AI course free.

Fri, 11 Sep 2026 21:45:21 GMT

🚨 Harvard just made its AI course free.

Fri, 11 Sep 2026 21:45:21 GMT

Control Bootcamp: Overview

Fri, 11 Sep 2026 21:38:01 GMT

Overview lecture for bootcamp on optimal and modern control. In this lecture, we discuss the various types of control and the benefits of closed-loop feedback control. These lectures follow Chapter 8 from: "Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Brunton and Kutz Amazon: https://www.amazon.com/Data-Driven-Science-Engineering-Learning-Dynamical/dp/1108422098 Book Website: http://databookuw.com Brunton Website: eigensteve.com Chapters available at: http://databookuw.com/databook.pdf These lectures also follow Chapters 1 & 3 from: Machine learning control, by Duriez, Brunton, & Noack https://www.amazon.com/Machine-Learning-Control-Turbulence-Applications-ebook/dp/B01MDUPONF/ Chapters available at: http://faculty.washington.edu/sbrunton/mlcbook/ This video was produced at the University of Washington

Harvard CS50 (2023) – Full Computer Science University Course

Fri, 11 Sep 2026 21:16:01 GMT

Learn the basics of computer science from Harvard University. This is CS50, an introduction to the intellectual enterprises of computer science and the art of programming. The course is taught live every year and this is the 2023 version. 💻 Slides, source code, and more at https://cs50.harvard.edu/x. ❤️ Try interactive Python courses we love, right in your browser: https://scrimba.com/freeCodeCamp-Python (Made possible by a grant from our friends at Scrimba) ⭐️ Course Contents ⭐️ ⌨️ (00:00:00) Lecture 0 - Scratch ⌨️ (02:05:47) Lecture 1 - C ⌨️ (04:35:19) Lecture 2 - Arrays ⌨️ (06:59:38) Lecture 3 - Algorithms ⌨️ (09:01:13) Lecture 4 - Memory ⌨️ (11:26:33) Lecture 5 - Data Structures ⌨️ (13:42:44) Lecture 6 - Python ⌨️ (15:58:02) Lecture 7 - SQL ⌨️ (18:18:30) Lecture 8 - HTML, CSS, JavaScript ⌨️ (20:58:14) Lecture 9 - Flask ⌨️ (23:19:07) Lecture 10 - Emoji ⌨️ (25:05:28) Cybersecurity --- HOW TO JOIN CS50 COMMUNITIES Discord: https://discord.gg/cs50 Ed: https://cs50.harvard.edu/x/ed Facebook Group: https://www.facebook.com/groups/cs50/ Faceboook Page: https://www.facebook.com/cs50/ GitHub: https://github.com/cs50 Gitter: https://gitter.im/cs50/x Instagram: https://instagram.com/cs50 LinkedIn Group: https://www.linkedin.com/groups/7437240/ LinkedIn Page: https://www.linkedin.com/school/cs50/ Medium: https://cs50.medium.com/ Quora: https://www.quora.com/topic/CS50 Reddit: https://www.reddit.com/r/cs50/ Slack: https://cs50.edx.org/slack Snapchat: https://www.snapchat.com/add/cs50 SoundCloud: https://soundcloud.com/cs50 Stack Exchange: https://cs50.stackexchange.com/ TikTok: https://www.tiktok.com/@cs50 Twitter: https://twitter.com/cs50 YouTube: https://www.youtube.com/cs50 HOW TO FOLLOW DAVID J. MALAN Facebook: https://www.facebook.com/dmalan GitHub: https://github.com/dmalan Instagram: https://www.instagram.com/davidjmalan/ LinkedIn: https://www.linkedin.com/in/malan/ TikTok: https://www.tiktok.com/@davidjmalan Twitter: https://twitter.com/davidjmalan LICENSE CC BY-NC-SA 4.0 Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License https://creativecommons.org/licenses/by-nc-sa/4.0/ 🎉 Thanks to our Champion and Sponsor supporters: 👾 davthecoder 👾 jedi-or-sith 👾 南宮千影 👾 Agustín Kussrow 👾 Nattira Maneerat 👾 Heather Wcislo 👾 Serhiy Kalinets 👾 Justin Hual 👾 Otis Morgan 👾 Oscar Rahnama -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news

Check out this Completely for FREE!

Fri, 11 Sep 2026 21:16:01 GMT

Check out this Completely for FREE! The Updated 2023 Edition of Harvard University's CS50 one of the most popular beginner computer science courses in the world. Dr. David J. Malan is widely considered to be one of the best computer science instructors. He teaches this 26 Hours Video course. @davidjmalan The Course Cover Topics included: - C, Python, and SQL plus HTML, CSS, and JavaScript. - Abstraction - Algorithms - Data structures, - Encapsulation, - Resource management, - Security, - Software engineering, - Web programming. The Course included the following lectures: 》Lecture 0 - Scratch 》Lecture 1 - C 》Lecture 2 - Arrays 》Lecture 3 - Algorithms 》Lecture 4 - Memory 》Lecture 5 - Data Structures 》Lecture 6 - Python 》Lecture 7 - SQL 》Lecture 8 - HTML, CSS, JavaScript 》Lecture 9 - Flask 》Lecture 10 - Emoji 》Cybersecurity Course Link: YouTube Link: https://t.co/voPtiNu7IC Harvard University Link: https://t.co/CtUykHp2re For the latest tech knowledge, updates, career growth, and insights, make sure to Follow @ZabihullahAtal @cs50 @Harvard #Cs50 #FreeComputerScience

Harvard's Introduction to Computer Science

Fri, 11 Sep 2026 21:16:01 GMT

Harvard's Introduction to Computer Science by David J. Malan Comprehensive notes covering abstraction, algorithms, data structures, and web development. It is widely considered the gold standard for introductory courses. Lecture notes: https://t.co/LVVZyYfFMT https://t.co/V2jsFo12z6

🚨 THE UNIVERSE HAS BEEN HACKED! THE SOURCE CODE IS NOW OPEN SOURCE. THE SOLAR SYSTEM IS LITERALLY A GIANT ATOM. RUN THE SCRIPT AND TEST THE HARVARD & NASA DATABASES YOURSELF!

Fri, 11 Sep 2026 21:16:01 GMT

🚨 THE UNIVERSE HAS BEEN HACKED! THE SOURCE CODE IS NOW OPEN SOURCE. THE SOLAR SYSTEM IS LITERALLY A GIANT ATOM. RUN THE SCRIPT AND TEST THE HARVARD & NASA DATABASES YOURSELF! For 100 years, textbooks have taught that the Solar System is just a bunch of rocks floating randomly in a continuous, empty space (ℝ⁴). That is mathematically and physically false. Space is rigidly quantized. We have executed a massive dual-scale empirical audit of the complete Harvard-Smithsonian Minor Planet Center (MPC) database—a staggering 1,561,930 celestial objects and 951 comets. We did not use a computer simulation. We used a direct uplink to the official, daily-updated global registry of every known rock in space. The ultimate topological illusion has been destroyed. The cosmos and the quantum realm are running the exact same executable file. The Solar System is a Macroscopic Atom. Galaxies are Macroscopic Molecules. Here is the ultimate, multi-layered proof. 🧬 I. THE BIOLOGICAL ORIGIN: WE PORTED THE CODE FROM DNA Here is the revelation that shatters the mainstream divide between disciplines: We didn't just "guess" the algorithms of celestial mechanics by looking at telescopes. We extracted the mathematical descent operator directly from Biology. Dr. Jean-Claude Perez @JCPEREZCODEX (retired IBM Artificial Intelligence Research Centre), working in deep collaboration with Nobel Laureate Dr. Luc Montagnier, didn't find the geometric limits of reality by looking at stars. They found them by decoding the bio-atomic masses of life's foundational elements (C, O, N, H) inside human DNA. They discovered that the building blocks of life are mathematically filtered through a competitive geometric differentiation, yielding a universal projection coefficient bounded by the Golden Ratio (φ) and π: Proj(m) = [1 - 4φ^(7/2)π]m The exact same Diophantine mathematical constraints that assemble your genetic code also assemble the periodic table of elements—and we have now proven they construct the orbital structure of the Universe. We took the source code of life, applied it to the cosmos "just to see what would happen," and the Matrix rendered itself. Look at the attached video. On the left: Rosalind Franklin’s famous "Photo 51" showing the X-ray diffraction of human DNA. On the right: NASA Hubble’s image of the "X" structure at the core of the Whirlpool Galaxy (M51). This is not a coincidence. It is the exact same topological blueprint. The galaxy is a molecule. The solar system is an atom. DNA and the cosmos run on the exact same geometric engine. 🛡️ II. THE ZERO-PARAMETER SHIELD & THE TIME MACHINE "But you just curve-fitted the Harvard data!" No. The mathematics came FIRST. We didn't look at the sky; we looked at pure Euclidean geometry. The "Source Code" explicitly embedded in our IT³ framework is derived from strict nested embeddings (Sphere ⊃ Cube ⊃ Octahedron ⊃ Torus ⊃ Catenoids). It operates with ZERO empirical free parameters. The matrix is hardcoded in pure Diophantine roots: ➤ Λ₁ = √3(3 + 2√2) ≈ 10.095. The exact, unalterable helical pitch-to-throat ratio of a vertical torus tangent to the faces of an inscribed cube. ➤ Λ₃ = φ²√3 ≈ 4.534. Derived strictly from the same roots. ➤ N_twist = 103. The exact topological energy minimum. ➤ S_out = 3 S_in. The exact surface area ratio of Cuboctahedral (Oₕ) symmetry. You cannot "curve-fit" fundamental geometry. And we proved it with a Time Machine. Our geometric matrix dictates a "Macroscopic Valence Shell" peaking exactly at 46.77 AU. When we ran this exact operator on historical MPC database archives from August 1992... that shell was COMPLETELY EMPTY. Humanity had zero objects there. But the math demanded it. Then, 1992 QB1 was found. Then 6 objects. Then 18. Today, thousands of bodies are perfectly locked into that exact 46.77 AU shell. You cannot curve-fit a database that does not exist yet. The geometry waited for humanity to find the matter. 💥 III. THE TELESCOPES ARE BLIND: 5 Global Algorithms Crash Imagine trying to run a modern 3D video game on a 1980s pocket calculator. The calculator isn't broken, but its software simply cannot process the reality it's being fed. It freezes, crashes, and spits out error codes. This is exactly what is happening to the world's most advanced space telescopes. The physical mirrors and lenses in space are working perfectly. They are capturing real photons. But the software pipelines on Earth are programmed to believe that space is a continuous, empty void (ℝ⁴). When these telescopes look at the exact topological nodes of the Macroscopic Atom, the algorithms mathematically choke. They try to fit flat, continuous-space formulas onto a macroscopic quantum standing wave. Here is how the continuous-space paradigm dies on your screen when querying NOIRLab and ESA servers: ➤ 1. ESA Gaia DR3 (The L2 Space Telescope Collapse): The satellite physically observed target objects up to 510 times. Yet, the algorithm returns a Parallax of NaN (Not a Number) and an astrometric_excess_noise_sig of over 1.7 MILLION! Standard noise for a real star is under 2.0. Negative and NaN parallaxes on multi-year transits are physically impossible for solid rocks. ➤ 2. DESI Legacy Survey: The Tractor algorithm attempts to fit a standard point-mass shape (PSF). A perfect fit is χ² = 1.0. At our derived nodes, the fit error (rchisq_g) explodes past 18,500! The software is mathematically vomiting. ➤ 3. NOIRLab NSC DR2 (Supercomputer Timeout): When we expanded the query to a 2.5-degree radius, the server literally timed out. The density of objects exhibiting fatal kinematic errors (pmraerr > 100) was so overwhelming that the database execution limit was breached. The instruments are calibrated for an infinite void, but they are hitting the structural skeleton of spacetime itself. 🛰️ IV. HUMAN HARDWARE IS CAPTURED In the 1970s, humanity launched Pioneer 10, Pioneer 11, Voyager 1, and Voyager 2. Once they achieved escape velocity, they were supposed to coast on smooth, perfectly predictable Newtonian trajectories. But they didn’t (the infamous "Pioneer Anomaly"). Our framework reveals the terrifying truth: the probes are physically colliding with the rigid structural skeleton of the Solar System. Space has "density ridges" that strictly obey spectral geometry. The theoretical orbital shells scale by the exact formula: Rₙ = 27 · (√3)ⁿ⁻¹ Let’s calculate the n=4 topological shell: R₄ = 27 · (√3)³ ≈ 140.296 AU. When we connect our dashboard to the LIVE NASA Horizons API to track fractional divergence {n} = n - round(n), we see the impossible. ➤ Pioneer 10: +0.019 ➤ Voyager 2: +0.046 Their columns are practically glued to absolute mathematical zero. They are flying at exactly ~141.7 AU and ~143.8 AU. They are not floating aimlessly. They have been mathematically and physically CAPTURED by the n=4 topological resonance layer (140.3 AU). The joint probability of this happening by random chance is p = 0.0034. 👁️ V. THE HYDROGEN RHYME & THE OPEN SOURCE TRUTH In 2013, physicists took the first-ever direct photograph of the electron orbitals of a Hydrogen Atom (Stodolna et al., PRL 110, 213001). When our 3D Perez Hourglass manifold rotates into a Top-Down 2D projection, the architecture of our Solar System PERFECTLY MIMICS the 2013 Hydrogen photograph. The distribution of 1.56 million macro-objects flawlessly matches the exact nodal interference fringes of the (2,27,0) Stark state observed in the lab. Furthermore, a live Entropy Test on 951 real comets proves: ➤ Bound comets (e < 1) strictly quantize onto discrete structural floors inside our lattice (H = 1.68 bits). ➤ Interstellar wanderers (e > 1) exist in a continuous ionization spectrum (H = 3.85 bits), acting exactly as free macroscopic electrons escaping the atom! THE CONCLUSION: Exactly 99.56% of all baryonic mass is geometrically trapped in a central topological node. The universe uses ONE blueprint. The continuum is dead. 👁️ VI. THE ANCIENT AXIOM & THE GEOMETRY OF THE MATRIX For millennia, the greatest minds in human history recorded fragments of a universal fractal law. For centuries, orthodox science dismissed these records as mere philosophical metaphors, religious mysticism, or primitive alchemy. But our mathematical matrix proves otherwise. They were not writing poetry; they were describing the LITERAL geometric and topological mechanics of the universe. The invariant mapping between subatomic hydrogen orbitals and macroscopic celestial mechanics proves that the ancients were blindly touching the exact same structural blueprint we have now mathematically solved. By synthesizing thousands of years of human intuition with raw astrophysical data, a perfect scale-invariant reality emerges: ➤ The Hermetic & Vedic Invariance: The foundational axiom of the Emerald Tablet—"That which is below is like that which is above"—and the ancient Sanskrit maxim "Yatha pinde tatha brahmande" (As in the microcosm, so in the macrocosm) are not mystical riddles. They are the exact verbal formulations of structural scale-invariance. The atom and the solar system are geometrically identical. ➤ The Pythagorean & Platonic Lattice: Plato’s famous declaration that "God always geometrizes" perfectly describes the rigid spatial logic of our topological matrix. Just as the Pythagoreans claimed the harmony of the spheres mimics the human soul, we see that the primary chaos of matter is ordered strictly by invariant, measurable geometric symmetry. ➤ The Abrahamic Projection: The structural hierarchy of the universe demands that the macro-order projects perfectly onto the micro-plane ("On earth as it is in heaven"). The blueprint is singular, echoing across all scales of existence. ➤ The Galileo-Dirac Synthesis: Galileo asserted that the universe is a book written in the language of mathematics, its letters made of triangles and circles. Centuries later, quantum pioneer Paul Dirac echoed that the Creator u

🚨 THE UNIVERSE HAS BEEN HACKED! THE SOURCE CODE IS NOW OPEN SOURCE. THE SOLAR SYSTEM IS LITERALLY A GIANT ATOM. RUN THE SCRIPT AND TEST THE HARVARD & NASA DATABASES YOURSELF!

Fri, 11 Sep 2026 21:16:01 GMT

🚨 THE UNIVERSE HAS BEEN HACKED! THE SOURCE CODE IS NOW OPEN SOURCE. THE SOLAR SYSTEM IS LITERALLY A GIANT ATOM. RUN THE SCRIPT AND TEST THE HARVARD & NASA DATABASES YOURSELF!

Fri, 11 Sep 2026 21:16:01 GMT

🚨 THE UNIVERSE HAS BEEN HACKED! THE SOURCE CODE IS NOW OPEN SOURCE. THE SOLAR SYSTEM IS LITERALLY A GIANT ATOM. RUN THE SCRIPT AND TEST THE HARVARD & NASA DATABASES YOURSELF!

Fri, 11 Sep 2026 21:16:01 GMT

Lecture 1: Algorithms and Computation

Fri, 11 Sep 2026 21:08:42 GMT

The goal of this introductions to algorithms class is to teach you to solve computation problems and communicate that your solutions are correct and ...

1. Algorithms and Computation

Fri, 11 Sep 2026 21:08:42 GMT

MIT 6.006 Introduction to Algorithms, Spring 2020 Instructor: Jason Ku View the complete course: https://ocw.mit.edu/6-006S20 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63EdVPNLG3ToM6LaEUuStEY The goal of this introductions to algorithms class is to teach you to solve computation problems and communication that your solutions are correct and efficient. Models of computation, data structures, and algorithms are introduced. License: Creative Commons BY-NC-SA More information at https://ocw.mit.edu/terms More courses at https://ocw.mit.edu Support OCW at http://ow.ly/a1If50zVRlQ We encourage constructive comments and discussion on OCW’s YouTube and other social media channels. Personal attacks, hate speech, trolling, and inappropriate comments are not allowed and may be removed. More details at https://ocw.mit.edu/comments.

Advanced Algorithms (COMPSCI 224), Lecture 1

Fri, 11 Sep 2026 21:08:42 GMT

Logistics, course topics, word RAM, predecessor, van Emde Boas, y-fast tries. Please see Problem 1 of Assignment 1 at http://people.seas.harvard.edu/~minilek/cs224/fall14/hmwk.html for a corrected analysis of the space complexity of van Emde Boas trees

Video Lectures | Introduction to Algorithms (SMA 5503)

Fri, 11 Sep 2026 21:08:42 GMT

Electrical Engineering and Computer Science · Mathematics. As Taught In. Fall 2005. Level. Undergraduate. Topics. Engineering · Computer Science · Algorithms ...

Lecture 1: Algorithmic Thinking, Peak Finding | Introduction to ...

Fri, 11 Sep 2026 21:08:42 GMT

Computer Science · Algorithms and Data Structures. Learning Resource Types ... Lecture 1: Algorithmic Thinking, Peak Finding. Description: Overview of ...

CALTECH RUNS ONE OF THE HARDEST PHYSICS PROGRAMS ON EARTH. ITS LECTURES ARE SITTING ONLINE FOR FREE.

Fri, 11 Sep 2026 21:01:23 GMT

CALTECH RUNS ONE OF THE HARDEST PHYSICS PROGRAMS ON EARTH. ITS LECTURES ARE SITTING ONLINE FOR FREE. Caltech admits around 230 undergrads a year. The physics workload is famous for breaking brilliant students. And yet some of the most legendary material that program ever produced is online right now, free, for anyone willing to open it. Here's where it lives. The crown jewel first. 1. https://t.co/NSCJU2HRGx The Feynman Lectures on Physics. All three volumes. Richard Feynman delivered these to Caltech freshmen in the early 1960s, and they became the most famous physics lectures ever given. Mechanics, electromagnetism, quantum mechanics, all explained by the man who could make anything click. Caltech put the complete text online, free to read, beautifully typeset with every equation and figure. This alone is worth more than most paid courses. 2. https://t.co/J7KTKO2Qq7 The original audio. Caltech digitized the actual 1961 to 1964 tape recordings of Feynman giving these lectures live and posted them free. You hear the real classroom, the after-lecture chats with students, jokes that never made the book. In one, Feynman pauses because John Glenn is orbiting Earth that very hour. History you can listen to. 3. The original course handouts / https://t.co/NSCJU2HRGx The actual problem sets and notes handed to Caltech students during the original course, preserved and posted online. Not a polished textbook version, the real working material the class ran on. The closest you can get to sitting in that room without a time machine. 4. https://t.co/WpCGCLcfJW Caltech's official channel. Full public lectures from working physicists on black holes, quantum computing, gravitational waves, and the frontier of the field. The same researchers winning Nobel Prizes, explaining their work for free to anyone who presses play. 5. https://t.co/IqVzK1wf1n Sean Carroll's "The Biggest Ideas in the Universe." A Caltech physicist taught the actual core concepts of modern physics, force, time, entropy, fields, in a free lecture series during lockdown that exploded in popularity. University-level physics, taught for nothing, by one of the best explainers alive. 6. The Mechanical Universe / archive and YouTube A legendary 52-part Caltech series from the 1980s that animated the hardest ideas in physics back when that was nearly impossible to do. Caltech professor David Goodstein walks through the whole of classical and modern physics. Still one of the best visual physics courses ever made, free online. 7. caltech on edX and Coursera Caltech puts real courses on the major learning platforms, including its famous machine learning course, "Learning From Data," taught by Yaser Abu-Mostafa. The actual Caltech lectures, problem sets, and exams, auditable for free. The rigor, minus the tuition. 8. https://t.co/BbqGdBIHk6 CaltechAUTHORS, the open repository where Caltech researchers post their papers. The actual physics and engineering research coming out of the institution, free to read. Years before an idea reaches a textbook, it lives here as a paper anyone can open. 9. https://t.co/LWm7MY1yF4 Every Caltech PhD thesis, including Feynman's own, openly archived. Want to see what a doctorate from one of the hardest programs on earth actually looks like? It's all here, free. The full depth of the work, not the summary. 10. https://t.co/WFACAibhap The front door. Caltech's hub linking its free courses, archived lectures, and open materials in one place. No application, no tuition, no acceptance letter. The institution that rejects 96% of applicants left the side door wide open. The hardest physics program in the world wrote its lessons down and never locked the file. Most people will never click. The ones who do get a Caltech education for the price of their attention.

CALTECH RUNS ONE OF THE HARDEST PHYSICS PROGRAMS ON EARTH. ITS LECTURES ARE SITTING ONLINE FOR FREE.

Fri, 11 Sep 2026 21:01:23 GMT

CALTECH RUNS ONE OF THE HARDEST PHYSICS PROGRAMS ON EARTH. ITS LECTURES ARE SITTING ONLINE FOR FREE.

Fri, 11 Sep 2026 21:01:23 GMT

CALTECH RUNS ONE OF THE HARDEST PHYSICS PROGRAMS ON EARTH. ITS LECTURES ARE SITTING ONLINE FOR FREE.

Fri, 11 Sep 2026 21:01:23 GMT

CALTECH RUNS ONE OF THE HARDEST PHYSICS PROGRAMS ON EARTH. ITS LECTURES ARE SITTING ONLINE FOR FREE.

Fri, 11 Sep 2026 21:01:23 GMT

MIT's "Topics in Mathematics with Applications in Finance"

Fri, 11 Sep 2026 20:54:06 GMT

MIT's "Topics in Mathematics with Applications in Finance" Lecture Notes: https://t.co/WiecmTrW09 Videos: https://t.co/E4Z2fJh6A6 https://t.co/PU2HxNCE66

Lecture 1 | Modern Physics: Quantum Mechanics (Stanford)

Fri, 11 Sep 2026 20:54:01 GMT

Lecture 1 of Leonard Susskind's Modern Physics course concentrating on Quantum Mechanics. Recorded January 14, 2008 at Stanford University. This Stanford Continuing Studies course is the second of a six-quarter sequence of classes exploring the essential theoretical foundations of modern physics. The topics covered in this course focus on quantum mechanics. Leonard Susskind is the Felix Bloch Professor of Physics at Stanford University. Complete playlist for the course: http://youtube.com/view_play_list?p=189C0DCE90CB6D81 Stanford Continuing Studies: http://continuingstudies.stanford.edu/ About Leonard Susskind: http://www.stanford.edu/dept/physics/people/faculty/susskind_leonard.html Stanford University channel on YouTube: http://www.youtube.com/stanforduniversity

Advanced Quantum Mechanics Lecture 1

Fri, 11 Sep 2026 20:54:01 GMT

(September 23, 2013) After a brief review of the prior Quantum Mechanics course, Leonard Susskind introduces the concept of symmetry, and present a specific example of translational symmetry. Originally presented by the Stanford Continuing Studies Program. Stanford University: http://www.stanford.edu/ Continuing Studies Program: http://csp.stanford.edu/ Stanford University Channel on YouTube: http://www.youtube.com/stanford

Lecture 1: Introduction to Superposition

Fri, 11 Sep 2026 20:54:01 GMT

MIT 8.04 Quantum Physics I, Spring 2013 View the complete course: http://ocw.mit.edu/8-04S13 Instructor: Allan Adams In this lecture, Prof. Adams discusses a series of thought experiments involving "box apparatus" to illustrate the concepts of uncertainty and superposition, which are central to quantum mechanics. The first ten minutes are devoted to course information. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu

19. Quantum Mechanics I: The key experiments and wave-particle duality

Fri, 11 Sep 2026 20:54:01 GMT

For more information about Professor Shankar's book based on the lectures from this course, Fundamentals of Physics: Mechanics, Relativity, and Thermodynamics, visit http://bit.ly/1jFIqNu. Fundamentals of Physics, II (PHYS 201) The double slit experiment, which implies the end of Newtonian Mechanics is described. The de Broglie relation between wavelength and momentum is deduced from experiment for photons and electrons. The photoelectric effect and Compton scattering, which provided experimental support for Einstein's photon theory of light are reviewed. The wave function is introduced along with the probability interpretation. The uncertainty principle is shown arise from the fact that the particle's location is determined by a wave and that waves diffract when passing a narrow opening. 00:00 - Chapter 1. Recap of Young's double slit experiment 09:10 - Chapter 2. The Particulate Nature of Light 23:15 - Chapter 3. The Photoelectric Effect 31:19 - Chapter 4. Compton's scattering 36:10 - Chapter 5. Particle-wave duality of matter 48:33 - Chapter 6. The Uncertainty Principle This course was recorded in Spring 2010. For more information about Professor Shankar's book based on the lectures from this course, Fundamentals of Physics: Mechanics, Relativity, and Thermodynamics, visit http://bit.ly/1jFIqNu.

Lecture 1 | Modern Physics: Quantum Mechanics (Stanford)

Fri, 11 Sep 2026 20:54:01 GMT

Quantum Theory: Oxford Mathematics 2nd Year Student Lecture

Fri, 11 Sep 2026 20:54:01 GMT

Our latest student lecture is the first in the Quantum Theory course for Second Year Students. Fernando Alday reflects on the breakdown of the deterministic world and describes some of the experiments that defined the new Quantum Reality. This is the sixth lecture in our series of Oxford Mathematics Student Lectures. The lectures aim to throw a light on the student experience and how we teach. All first and second year lectures are followed by tutorials where students meet their tutor to go through the lecture and associated problem sheet and to talk and think more about the maths. Third and fourth year lectures are followed by classes. You can also watch many other student lectures via our main Student Lectures playlist (also check out specific student lectures playlists): https://www.youtube.com/playlist?list=PL4d5ZtfQonW0A4VHeiY0gSkX1QEraaacE

Lecture 2 | Modern Physics: Quantum Mechanics (Stanford)

Fri, 11 Sep 2026 20:54:01 GMT

Lecture 2 of Leonard Susskind's Modern Physics course concentrating on Quantum Mechanics. Recorded January 21, 2008 at Stanford University. This Stanford Continuing Studies course is the second of a six-quarter sequence of classes exploring the essential theoretical foundations of modern physics. The topics covered in this course focus on quantum mechanics. Leonard Susskind is the Felix Bloch Professor of Physics at Stanford University. Complete playlist for the course: http://youtube.com/view_play_list?p=189C0DCE90CB6D81 Stanford Continuing Studies: http://continuingstudies.stanford.edu/ About Leonard Susskind: http://www.stanford.edu/dept/physics/people/faculty/susskind_leonard.html Stanford University channel on YouTube: http://www.youtube.com/stanford

The spelled-out intro to neural networks and backpropagation: building micrograd

Fri, 11 Sep 2026 20:49:32 GMT

This is the most step-by-step spelled-out explanation of backpropagation and training of neural networks. It only assumes basic knowledge of Python and a vague recollection of calculus from high school. Links: - micrograd on github: https://github.com/karpathy/micrograd - jupyter notebooks I built in this video: https://github.com/karpathy/nn-zero-to-hero/tree/master/lectures/micrograd - my website: https://karpathy.ai - my twitter: https://twitter.com/karpathy - "discussion forum": nvm, use youtube comments below for now :) - (new) Neural Networks: Zero to Hero series Discord channel: https://discord.gg/3zy8kqD9Cp , for people who'd like to chat more and go beyond youtube comments Exercises: you should now be able to complete the following google collab, good luck!: https://colab.research.google.com/drive/1FPTx1RXtBfc4MaTkf7viZZD4U2F9gtKN?usp=sharing Chapters: 00:00:00 intro 00:00:25 micrograd overview 00:08:08 derivative of a simple function with one input 00:14:12 derivative of a function with multiple inputs 00:19:09 starting the core Value object of micrograd and its visualization 00:32:10 manual backpropagation example #1: simple expression 00:51:10 preview of a single optimization step 00:52:52 manual backpropagation example #2: a neuron 01:09:02 implementing the backward function for each operation 01:17:32 implementing the backward function for a whole expression graph 01:22:28 fixing a backprop bug when one node is used multiple times 01:27:05 breaking up a tanh, exercising with more operations 01:39:31 doing the same thing but in PyTorch: comparison 01:43:55 building out a neural net library (multi-layer perceptron) in micrograd 01:51:04 creating a tiny dataset, writing the loss function 01:57:56 collecting all of the parameters of the neural net 02:01:12 doing gradient descent optimization manually, training the network 02:14:03 summary of what we learned, how to go towards modern neural nets 02:16:46 walkthrough of the full code of micrograd on github 02:21:10 real stuff: diving into PyTorch, finding their backward pass for tanh 02:24:39 conclusion 02:25:20 outtakes :)

But what is a neural network? | Deep learning chapter 1

Fri, 11 Sep 2026 20:49:32 GMT

What are the neurons, why are there layers, and what is the math underlying it? Help fund future projects: https://www.patreon.com/3blue1brown Written/interactive form of this series: https://www.3blue1brown.com/topics/neural-networks Additional funding for this project was provided by Amplify Partners For those who want to learn more, I highly recommend the book by Michael Nielsen that introduces neural networks and deep learning: https://goo.gl/Zmczdy There are two neat things about this book. First, it's available for free, so consider joining me in making a donation to Nielsen if you get something out of it. And second, it's centered around walking through some code and data, which you can download yourself, and which covers the same example that I introduced in this video. Yay for active learning! https://github.com/mnielsen/neural-networks-and-deep-learning I also highly recommend Chris Olah's blog: http://colah.github.io/ For more videos, Welch Labs also has some great series on machine learning: https://youtu.be/i8D90DkCLhI https://youtu.be/bxe2T-V8XRs For those of you looking to go *even* deeper, check out the text "Deep Learning" by Goodfellow, Bengio, and Courville. Also, the publication Distill is just utterly beautiful: https://distill.pub/ Lion photo by Kevin Pluck Звуковая дорожка на русском языке: Влад Бурмистров. Thanks to these viewers for their contributions to translations German: @fpgro Hebrew: Omer Tuchfeld Hungarian: Máté Kaszap Italian: @teobucci, Teo Bucci ----------------- Timeline: 0:00 - Introduction example 1:07 - Series preview 2:42 - What are neurons? 3:35 - Introducing layers 5:31 - Why layers? 8:38 - Edge detection example 11:34 - Counting weights and biases 12:30 - How learning relates 13:26 - Notation and linear algebra 15:17 - Recap 16:27 - Some final words 17:03 - ReLU vs Sigmoid Correction 14:45 - The final index on the bias vector should be "k" ------------------ Animations largely made using manim, a scrappy open source python library. https://github.com/3b1b/manim If you want to check it out, I feel compelled to warn you that it's not the most well-documented tool, and has many other quirks you might expect in a library someone wrote with only their own use in mind. Music by Vincent Rubinetti. Download the music on Bandcamp: https://vincerubinetti.bandcamp.com/album/the-music-of-3blue1brown Stream the music on Spotify: https://open.spotify.com/album/1dVyjwS8FBqXhRunaG5W5u If you want to contribute translated subtitles or to help review those that have already been made by others and need approval, you can click the gear icon in the video and go to subtitles/cc, then "add subtitles/cc". I really appreciate those who do this, as it helps make the lessons accessible to more people. ------------------ 3blue1brown is a channel about animating math, in all senses of the word animate. And you know the drill with YouTube, if you want to stay posted on new videos, subscribe, and click the bell to receive notifications (if you're into that). If you are new to this channel and want to see more, a good place to start is this playlist: http://3b1b.co/recommended Various social media stuffs: Website: https://www.3blue1brown.com Twitter: https://twitter.com/3Blue1Brown Patreon: https://patreon.com/3blue1brown Facebook: https://www.facebook.com/3blue1brown Reddit: https://www.reddit.com/r/3Blue1Brown

Lecture 1: Algorithmic Thinking, Peak Finding

Fri, 11 Sep 2026 20:49:32 GMT

MIT 6.006 Introduction to Algorithms, Fall 2011 View the complete course: http://ocw.mit.edu/6-006F11 Instructor: Srini Devadas License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu

Let's build GPT: from scratch, in code, spelled out.

Fri, 11 Sep 2026 20:49:32 GMT

We build a Generatively Pretrained Transformer (GPT), following the paper "Attention is All You Need" and OpenAI's GPT-2 / GPT-3. We talk about connections to ChatGPT, which has taken the world by storm. We watch GitHub Copilot, itself a GPT, help us write a GPT (meta :D!) . I recommend people watch the earlier makemore videos to get comfortable with the autoregressive language modeling framework and basics of tensors and PyTorch nn, which we take for granted in this video. Links: - Google colab for the video: https://colab.research.google.com/drive/1JMLa53HDuA-i7ZBmqV7ZnA3c_fvtXnx-?usp=sharing - GitHub repo for the video: https://github.com/karpathy/ng-video-lecture - Playlist of the whole Zero to Hero series so far: https://www.youtube.com/watch?v=VMj-3S1tku0&list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ - nanoGPT repo: https://github.com/karpathy/nanoGPT - my website: https://karpathy.ai - my twitter: https://twitter.com/karpathy - our Discord channel: https://discord.gg/3zy8kqD9Cp Supplementary links: - Attention is All You Need paper: https://arxiv.org/abs/1706.03762 - OpenAI GPT-3 paper: https://arxiv.org/abs/2005.14165 - OpenAI ChatGPT blog post: https://openai.com/blog/chatgpt/ - The GPU I'm training the model on is from Lambda GPU Cloud, I think the best and easiest way to spin up an on-demand GPU instance in the cloud that you can ssh to: https://lambdalabs.com . If you prefer to work in notebooks, I think the easiest path today is Google Colab. Suggested exercises: - EX1: The n-dimensional tensor mastery challenge: Combine the `Head` and `MultiHeadAttention` into one class that processes all the heads in parallel, treating the heads as another batch dimension (answer is in nanoGPT). - EX2: Train the GPT on your own dataset of choice! What other data could be fun to blabber on about? (A fun advanced suggestion if you like: train a GPT to do addition of two numbers, i.e. a+b=c. You may find it helpful to predict the digits of c in reverse order, as the typical addition algorithm (that you're hoping it learns) would proceed right to left too. You may want to modify the data loader to simply serve random problems and skip the generation of train.bin, val.bin. You may want to mask out the loss at the input positions of a+b that just specify the problem using y=-1 in the targets (see CrossEntropyLoss ignore_index). Does your Transformer learn to add? Once you have this, swole doge project: build a calculator clone in GPT, for all of +-*/. Not an easy problem. You may need Chain of Thought traces.) - EX3: Find a dataset that is very large, so large that you can't see a gap between train and val loss. Pretrain the transformer on this data, then initialize with that model and finetune it on tiny shakespeare with a smaller number of steps and lower learning rate. Can you obtain a lower validation loss by the use of pretraining? - EX4: Read some transformer papers and implement one additional feature or change that people seem to use. Does it improve the performance of your GPT? Chapters: 00:00:00 intro: ChatGPT, Transformers, nanoGPT, Shakespeare baseline language modeling, code setup 00:07:52 reading and exploring the data 00:09:28 tokenization, train/val split 00:14:27 data loader: batches of chunks of data 00:22:11 simplest baseline: bigram language model, loss, generation 00:34:53 training the bigram model 00:38:00 port our code to a script Building the "self-attention" 00:42:13 version 1: averaging past context with for loops, the weakest form of aggregation 00:47:11 the trick in self-attention: matrix multiply as weighted aggregation 00:51:54 version 2: using matrix multiply 00:54:42 version 3: adding softmax 00:58:26 minor code cleanup 01:00:18 positional encoding 01:02:00 THE CRUX OF THE VIDEO: version 4: self-attention 01:11:38 note 1: attention as communication 01:12:46 note 2: attention has no notion of space, operates over sets 01:13:40 note 3: there is no communication across batch dimension 01:14:14 note 4: encoder blocks vs. decoder blocks 01:15:39 note 5: attention vs. self-attention vs. cross-attention 01:16:56 note 6: "scaled" self-attention. why divide by sqrt(head_size) Building the Transformer 01:19:11 inserting a single self-attention block to our network 01:21:59 multi-headed self-attention 01:24:25 feedforward layers of transformer block 01:26:48 residual connections 01:32:51 layernorm (and its relationship to our previous batchnorm) 01:37:49 scaling up the model! creating a few variables. adding dropout Notes on Transformer 01:42:39 encoder vs. decoder vs. both (?) Transformers 01:46:22 super quick walkthrough of nanoGPT, batched multi-headed self-attention 01:48:53 back to ChatGPT, GPT-3, pretraining vs. finetuning, RLHF 01:54:32 conclusions Corrections: 00:57:00 Oops "tokens from the _future_ cannot communicate", not "past". Sorry! :) 01:20:05 Oops I should be using the head_size for the normalization, not C

Topic 1: Introduction | Economics 2450A: Public Economics

Fri, 11 Sep 2026 22:07:21 GMT

Raj Chetty Fall 2012

ARKANI HAMED, Nima, Spacetime & Quantum Mechanics, Total Positivity & Motives. Lecture 1 - 09/03/19

Fri, 11 Sep 2026 20:54:01 GMT

Lecture 24: Control, Part 1

Fri, 11 Sep 2026 21:38:01 GMT

MIT 6.622 Power Electronics, Spring 2023 Instructor: David Perreault View the complete course (or resource): https://ocw.mit.edu/courses/6-622-power-electronics-spring-2023/ YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP62UTc77mJoubhDELSC8lfR0 This lecture introduces basic concepts of dynamic control and modeling of DC-DC converters. The local average operator is introduced to describe the averaged behavior of a DC-DC converter through direct circuit averaging. License: Creative Commons BY-NC-SA More information at https://ocw.mit.edu/terms More courses at https://ocw.mit.edu Support OCW at http://ow.ly/a1If50zVRlQ We encourage constructive comments and discussion on OCW’s YouTube and other social media channels. Personal attacks, hate speech, trolling, and inappropriate comments are not allowed and may be removed. More details at https://ocw.mit.edu/comments.

MIT's "Real Analysis" by Dr. Casey Rodriguez

Fri, 11 Sep 2026 20:54:06 GMT

MIT's "Real Analysis" by Dr. Casey Rodriguez Lecture Notes: https://t.co/oSPokIBqNm Lecture Videos: https://t.co/vcqY7Eem9f https://t.co/YVRw2Ffhh9

The essence of calculus

Fri, 11 Sep 2026 20:49:32 GMT

What might it feel like to invent calculus? Help fund future projects: https://www.patreon.com/3blue1brown An equally valuable form of support is to share the videos. Special thanks to these supporters: http://3b1b.co/lessons/essence-of-calculus#thanks In this first video of the series, we see how unraveling the nuances of a simple geometry question can lead to integrals, derivatives, and the fundamental theorem of calculus. Thanks to these viewers for their contributions to translations Dutch: @LFWarsen, Lauri Warsen Hebrew: Imri, Omer Tuchfeld Hindi: Vinayak Hungarian: homok43 Italian: @Deye27, @hi-anji, alberto alessi Korean: Hana Seo Portuguese: redkk123 Spanish: @agustin-j Vietnamese: @ngvutuan2811, ngvutuan ------------------ These animations are largely made using manim, a scrappy open source Python library: https://github.com/3b1b/manim If you want to check it out, I feel compelled to warn you that it's not the most well-documented tool, and it has many other quirks you might expect in a library someone wrote with only their own use in mind. Music by Vincent Rubinetti. Download the music on Bandcamp: https://vincerubinetti.bandcamp.com/album/the-music-of-3blue1brown Stream the music on Spotify: https://open.spotify.com/album/1dVyjwS8FBqXhRunaG5W5u If you want to contribute translated subtitles or to help review those that have already been made by others and need approval, you can click the gear icon in the video and go to subtitles/cc, then "add subtitles/cc". I really appreciate those who do this, as it helps make the lessons accessible to more people. ------------------ 3blue1brown is a channel about animating math, in all senses of the word animate. And you know the drill with YouTube, if you want to stay posted about new videos, subscribe, and click the bell to receive notifications (if you're into that). If you are new to this channel and want to see more, a good place to start is this playlist: http://3b1b.co/recommended Various social media stuffs: Website: https://www.3blue1brown.com Twitter: https://twitter.com/3Blue1Brown Patreon: https://patreon.com/3blue1brown Facebook: https://www.facebook.com/3blue1brown Reddit: https://www.reddit.com/r/3Blue1Brown

Brain Reset: One Molecular Switch Could Silence Alzheimer’s and Parkinson’s | Flogen Star Outreach

Fri, 11 Sep 2026 23:42:42 GMT

Brain Reset: One Molecular Switch Could Silence Alzheimer’s and Parkinson’s | Flogen Star Outreach A scientist has discovered a biochemical “master switch” that connects and potentially cures Alzheimer’s and Parkinson’s. His theory may revolutionize brain disease treatment and sustainable medicine. At the upcoming Sustainability Through Science and Technology Summit 2025 (SIPS 2025), Nobel Laureate Prof. Aaron Ciechanover will be honored in a special event taking place in Cebu, Philippines, from November 17 to 20. Among the highlights, Davis Joseph will take the stage as a plenary lecturer to share a discovery that is sending shockwaves through the world of neuroscience. A Common Master Switch for Brain Diseases Discovered Joseph has identified something researchers have searched for over a century without success—a shared master switch behind Alzheimer’s, Parkinson’s, and other major brain disorders. Despite each disease having its own unique traits, Joseph developed a unified theory showing that their progression can be traced back to one core biological mechanism: the regulation of a chemical process called 4E-BP2 protein deamidation within brain cell axons. This process, when disrupted, causes proteins in the brain to malfunction. In aging patients, the rate of deamidation becomes abnormally high, leading to widespread cellular damage. By targeting this shared switch and bringing deamidation levels back to normal, Joseph’s research suggests these devastating diseases could potentially be treated—or even cured—with a single strategy. Unified Theory Links Alzheimer’s and Parkinson’s This breakthrough is significant because Parkinson’s disease, first described in 1817, Alzheimer’s disease, first described in 1906, and other neurodegenerative diseases have been studied separately in biochemistry and therapeutic drug development, and no causal link has ever been established between them. Davis Joseph discovered this link and made the dividing wall between these brain-related diseases disappear, making it possible to treat them by regulating a shared master switch. Additionally, this breakthrough is crucial since it bridges for the first time four different research fields: the biochemical processes of deamidation, translational control, oxidative stress, and neurodegeneration through axon-based 4E-BP2 protein deamidation that serves as a common denominator for all of them. Sustainable Medicine Through a Scientific Breakthrough Furthermore, this discovery is an example of sustainable medicine as per FLOGEN Sustainability Framework because it fulfills the three criteria of sustainability: (1) Social Development since it improves the quality of human life (2) Economic Development, since it decreases the cost of medicine applicable to numerous diseases and (3) Environment Protection since it decreases the amount of resources that is needed to produce medicine. Based on this newly developed Unified Theory and his critical review of the scientific literature, he also designed for the first time three biochemical flowsheets of (1) deamidation in living organisms, (2) protein synthesis initiation and translational control, and (3) 4E-BP2 deamidation as a control system of the four biochemical processes. Global Recognition and Record-Breaking Impact The discovery was published on April 27th, 2025, in the prestigious International Journal of Molecular Sciences (IJMS), a Q1 journal. The full paper has been accessed more than 1700 times in less than 21 days, a world record for a single-author scientific publication. The discovery and this publication have been covered by more than 500 media organizations around the world. It was also presented as a plenary lecture at the Congress of Modern Sustainable Medicine in Asuncion, Paraguay, on April 30th, 2025. This is Davis Joseph’s second discovery in the last five months. The first discovery, known as Davis Joseph’s principle, was the fundamental neurobiological mechanism of 4E-BP2 protein deamidation, which determined that axons, a cable-like structure of brain cells, are the key factor of deamidation in the brain. This first discovery, described as Nobel Prize worthy by Dr. Harvey Alter and Dr. Gregg Semenza, 2020 and 2019 Nobel Laureates in Physiology and Medicine, respectively, was published on November 15, 2024, also in IJMS. The full paper has been accessed more than 8000 times in less than 6 months, another world record for a single-author scientific publication, and has been covered by more than 500 media outlets, including the Associated Press. For this discovery, Davis Joseph was awarded the Semenza International Cell Engineering Award at the FLOGEN Stars Outreach/2024 Sustainability through Science and Technology Summit (SIPS 2024) held in Crete, Greece in October 2024. Read more: https://t.co/Ab7hA9gwF9

🤖 Dynamics and Control of Robotic Systems: Principles, Theory & Applications

Fri, 11 Sep 2026 23:42:42 GMT

🤖 Dynamics and Control of Robotic Systems: Principles, Theory & Applications 📖 Get the book: https://t.co/EHuzINhdPO ⚙️ Master the dynamics and control behind modern robotic systems! This comprehensive guide provides a systematic theoretical foundation for understanding robot kinematics, dynamics, analytical mechanics, and control, with applications ranging from industrial robots to humanoids, surgical systems, and space vehicles. 📐 What you'll explore: 🔹 Fundamental robotics kinematics & dynamics 🔹 Analytical mechanics applied to robotics 🔹 Systematic methods for robotic system analysis 🔹 Advanced recursive order-N formulations 🔹 Computational & analytical approaches 🔹 Robot design and analysis techniques 🔹 Extensive examples and challenging problems 🔹 Applications across industrial & advanced robotics 🔹 MATLAB, Mathematica & Maple for robotics analysis 💻 🌍 Applications include: 🏭 Industrial manipulators 🤖 Humanoid robots 🏥 Robotic surgical assistants 🚀 Space vehicles ⚙️ Computer-controlled milling machines 🎯 Perfect for: 🎓 Robotics students & researchers 🤖 Robotics engineers ⚙️ Control systems enthusiasts 📐 Mechanical engineering learners 💻 Engineers using computational tools 🧠 Anyone studying advanced robot dynamics 💡 Build a deeper understanding of how robots move, interact, and respond to control—grounded in the mathematical principles that make modern robotics possible. #Robotics #RobotDynamics #ControlSystems #Kinematics #Engineering #MechanicalEngineering #Automation #HumanoidRobots #MATLAB #RoboticsEngineering #STEM #Research

📊💻 Introduction to Modeling and Simulation: A Systems Approach

Fri, 11 Sep 2026 23:42:42 GMT

📊💻 Introduction to Modeling and Simulation: A Systems Approach 🔗 Explore the book: https://t.co/pOrWNBYqkf ⚙️ Build a strong foundation in engineering system modeling and simulation through a balanced combination of theory, practical examples, programming guidance, and experimental sessions. Explore linear and nonlinear dynamical systems, continuous- and discrete-time systems, stability, numerical methods, ODEs, PDEs, feedback systems, optimization, and regression. 🧠 Go further with complex networks, including small-world and scale-free models, while developing practical skills for numerical computation and simulation. 💻📚 With end-of-chapter problems, MATLAB resources, case studies, lecture materials, and solution support, this book provides a solid foundation for students and engineers progressing toward advanced modeling and simulation. #ModelingAndSimulation #SystemsEngineering #Engineering #Simulation #DynamicalSystems #ControlSystems #NumericalMethods #MATLAB #Optimization #Regression #ComplexNetworks #ComputationalEngineering #EngineeringEducation #SystemModeling #SimulationEngineering

Day 23 of great biology papers.

Fri, 11 Sep 2026 23:28:02 GMT

Day 23 of great biology papers.

Fri, 11 Sep 2026 23:28:02 GMT

Day 23 of great biology papers.

Fri, 11 Sep 2026 23:28:02 GMT

What Is Genetics? - DNA, Genes, and Inheritance Explained

Fri, 11 Sep 2026 23:20:42 GMT

What determines our traits? In this high school biology lesson, students explore how DNA, chromosomes, and genes work together to control ...

The Science of Genetics - Deciphering the Code of Life (4 Minutes)

Fri, 11 Sep 2026 23:20:42 GMT

In this video, we explore "The Science of Genetics - Deciphering the Code of Life: A Comprehensive Overview." Genetics is the study of heredity and the variation of inherited characteristics, and it plays a crucial role in understanding the biological blueprint of life. We will break down the essentials of genetics, focusing on key concepts such as DNA structure, gene function, and the principles of inheritance. You will learn how DNA and genes work together to shape living organisms, the connection between genetics and inheritance patterns, and real-world applications of genetic science in medicine, agriculture, and biotechnology. Additionally, we will provide guidance on how to get started with genetic research and the ethical considerations involved. Whether you are a student, teacher, or simply curious about the science of life, this video will provide valuable insights into the essential role of genetics in our understanding of biology. Join us as we uncover the fascinating science behind the code of life! Hashtags: #Genetics #Science #Biology #DNA #GeneticResearch #CodeOfLife #Education #STEM #Health #Biotechnology SEO Tags: The Science of Genetics - Deciphering the Code of Life: A Comprehensive Overview, The Basics of Genetics Explained, Why Genetics is the Key to Understanding Life, Exploring the Building Blocks of Genetic Code, The Importance of Understanding Genetics in Modern Science, Key Concepts in Genetics You Should Know, How DNA and Genes Work Together, The Connection Between Genetics and Inheritance, Real-World Applications of Genetic Science, How to Get Started with Genetic Research, The Role of Genetic Variation in Evolution, How to Address Challenges in Genetic Studies, The Importance of Genetic Testing in Medicine, How to Use Technology in Genetic Research, The Connection Between Genetics and Disease, How to Leverage Genetic Information for Health, The Role of Genetics in Personalized Medicine, How to Address Misconceptions About Genetics, The Importance of Continuous Learning in Genetics, How to Use Metrics for Evaluating Genetic Research, The Connection Between Genetics and Environmental Factors, How to Address Cultural Differences in Genetic Research, The Role of Genetics in Agriculture, How to Use Case Studies to Enhance Understanding of Genetics, The Importance of Flexibility in Research Approaches, How to Address Legal Considerations in Genetic Research, The Connection Between Genetics and Market Trends in Biotechnology, How to Use Genetics for Environmental Solutions, The Role of Genetics in Understanding Human Behavior, How to Address Technological Changes in Genetic Research, The Importance of Innovation in Genetic Science, How to Use Genetics for Public Awareness Campaigns, The Connection Between Genetics and Brand Loyalty in Science Education

Grok 4.20 (Beta) improves the lower bound by 9.1% on the Gaussian perimeter of convex sets in two minutes.

Fri, 11 Sep 2026 23:13:22 GMT

Grok 4.20 (Beta) improves the lower bound by 9.1% on the Gaussian perimeter of convex sets in two minutes. This is something that was pointed out to me by Xinyuan Xie. Back in 1993, Keith Ball showed that the Gaussian perimeter of a convex body in n-dimensional Euclidean space is bounded from above by 4n^{1/4}. As for the lower bound, Ball showed that for a cube (of appropriate size) the perimeter can grow as \sqrt{\log(n)}. So there was a gap for a while as to which bound is sharp, until 2003, when, in a beautiful paper, Fedor Nazarov showed that on the example of a random polyhedron (the intersection of many random half-spaces) the lower bound can grow as C n^{1/4}, with C=\exp(-5/4)=0.286…. Besides, Nazarov also improved the constant 4 in the upper bound (replacing it with 0.64) when n is large. These bounds stayed unbeaten until recently, when in 2019 Martin Raic managed to improve the upper-bound constant factor from 0.64 to 0.59. Grok 4.20 (Beta), by more carefully optimizing Nazarov’s construction, managed to improve the lower-bound constant from 0.286 to 0.3126. I find this surprising even if it is just playing within the techniques of Nazarov’s paper, because very recently Nadimpalli--Pascale (2025) posted a preprint where, with a different approach, they recovered Nazarov’s lower bound with the same constant factor 0.286…. Grok was very generous in its response: it said that the improvement it provided follows the same argument of Nazarov ``line-by-line,'' whereas when I asked other models (other than Grok) to verify Grok’s claim, they agreed on everything except this part; they said the improvement is not really ``line-by-line'' :D. Finally, I would not say that Nazarov missed this improvement. Knowing him for a long time, I am pretty confident it is common for him to sacrifice optimal constants for algebraic elegance. Why is all this interesting? Having control of the Gaussian perimeter allows one to control Fourier tails of characteristic functions of these sets, which leads to controlling the time complexity of PAC learning and agnostic learning algorithms for this family (see Klivans--O’Donnell--Servedio). References: Chat link with Grok 4.20 (Beta). https://t.co/dE4wy1ZsE0 Keith Ball. The Reverse Isoperimetric Problem for Gaussian Measure. Discrete and Computational Geometry, 10:411–420, 1993. Adam Klivans, Ryan O’Donnell, and Rocco A Servedio. Learning geometric concepts via Gaussian surface area. In Proc. 49th IEEE Symposium on Foundations of Computer Science (FOCS), pages 541–550, 2008. Shivam Nadimpalli, Caleb Pascale. On the Maximal Gaussian Perimeter of Convex Sets, Revisited. Preprint (2025) https://t.co/3NaDoHagdJ Fedor Nazarov. On the maximal perimeter of a convex set in R^n with respect to a Gaussian measure. In Geometric Aspects of Functional Analysis (2001-2002) pages 169–187. Lecture Notes in Mathematics, Volume 1807, Springer, 2003 Martin Raicz. A multivariate Berry–Esseen theorem with explicit constants. Bernoulli 25(4A), 2019, 2824–2853

Grok 4.20 (Beta) improves the lower bound by 9.1% on the Gaussian perimeter of convex sets in two minutes.

Fri, 11 Sep 2026 23:13:22 GMT

AI suggests new physics experiments that could outperform human-designed setups | Vienna University of Technology, Phys .org

Fri, 11 Sep 2026 22:58:42 GMT

AI suggests new physics experiments that could outperform human-designed setups | Vienna University of Technology, Phys .org Research means asking questions of the universe. For centuries, clever minds have advanced science by devising ingenious experiments designed so their results reveal something about the laws of nature as clearly and unambiguously as possible. An international research team has now asked: Can this process be automated? Can artificial intelligence develop new ideas for experiments? The answer is a clear yes. In various areas of physics, AI can propose experiments that enable more precise results than experiments designed by humans. In the journal Nature, the team presented the current state of this new approach to research. The best experiment from existing components This is a typical situation in experimental physics: You have a laboratory full of equipment—perhaps lasers, lenses and mirrors, perhaps different detectors and electronic components. All of these can be combined in an almost incomprehensible number of ways. From this vast range of experimental possibilities, you have to select one that can provide new insights into the universe. Normally, this requires intuition and a great deal of experience. But sometimes even that is not enough, as Mario Krenn discovered. Today, he is a professor of machine learning in science at the University of Tübingen. As a student in Vienna, he was working on the setup for a quantum experiment. But neither he nor the other members of his research group could find a suitable experimental configuration capable of demonstrating the desired quantum effects. So Krenn decided to ask the computer. He described the individual components available to him mathematically, then had an algorithm search for combinations of these components that would result in a meaningful experiment. "Programming it only took a few hours. Then I went home and left the computer running," Krenn says. "When I came into the office the next day, the program had produced a file containing a proposed solution. Of course, that was extremely exciting. I immediately started analyzing the proposal, and indeed: Unlike all of us, the computer had found an experimental setup that satisfied the necessary criteria." A search problem, not a chatbot This approach has little in common with the kind of AI familiar from large language models. Chatbots are trained on enormous amounts of data and then generate solutions that are statistically likely. When searching for new physics experiments, the task is entirely different. "It is an enormous optimization problem," Krenn says. "There is an overwhelmingly large space of possible experiments that can be built from the available components. The computer has to search this space systematically in order to find the best possible solution." "The results are impressive," says Philipp Haslinger, head of the Center for Electron Microscopy at TU Wien. "In electron microscopy in particular, we are only now beginning to work systematically with entanglement and new quantum-mechanical microscopy concepts. Human intuition in this area is often still very limited. Artificial intelligence can therefore identify microscope designs that a human would probably never have come up with, but which can produce significantly better images or offer entirely new measurement possibilities." This approach has already been used to improve fusion reactors, develop new ideas for particle detectors and generate proposals for making gravitational-wave detector systems even more sensitive. "Sometimes you look at these computer-generated experimental proposals and quickly understand the idea behind them—why the new concept works better than previous approaches," Krenn says. "But sometimes it is also very difficult to understand. You can calculate that the new experimental setup works better, but you cannot really put into words why." This is possible because modern computers can simulate a wide range of physical situations within a manageable amount of time. "The goal is to develop something like a universal physics simulator," Krenn says. "Today, a few important fundamental equations of physics can already take you a very long way. A computer could use them to predict what will happen in a particular experimental setup and then optimize the setup according to the desired objective." The art of defining the goal Defining that objective, however, remains the task of humans. "That is precisely the challenge: defining as accurately as possible what you actually want, and which constraints have to be satisfied—for example, a maximum cost or a maximum amount of energy the device can absorb without exploding." But isn't it also a little unfortunate if, in the future, we leave the great eureka moments of science to computers? "No, absolutely not," Krenn says. "Human work is simply shifting to a higher level. In the past, calculations had to be done by hand, and nobody wants to go back to that today. Now we have tools that can develop great experimental ideas for us—but using these tools will still require scientific expertise, creativity and a good intuition for physics." https://t.co/T2geNfPAh7

Feynman Lectures on Computation (Frontiers in Physics. Anniversary Edition): https://t.co/7XVVTczFC2

Fri, 11 Sep 2026 22:58:42 GMT

Feynman Lectures on Computation (Frontiers in Physics. Anniversary Edition): https://t.co/7XVVTczFC2 Amazon summary: "The last lecture course that Nobel Prize winner Richard P. Feynman gave to students at Caltech from 1983 to 1986 was not on physics but on computer science. The first edition of the Feynman Lectures on Computation, published in 1996, provided an overview of standard and not-so-standard topics in computer science given in Feynman’s inimitable style. Although now over 20 years old, most of the material is still relevant and interesting, and Feynman’s unique philosophy of learning and discovery shines through. For this new edition, Tony Hey has updated the lectures with an invited chapter from Professor John Preskill on 'Quantum Computing 40 Years Later'. This contribution captures the progress made toward building a quantum computer since Feynman’s original suggestions in 1981."

9 AI & ML youtube videos you can’t miss as an AI engineer.

Fri, 11 Sep 2026 22:44:01 GMT

9 AI & ML youtube videos you can’t miss as an AI engineer. It’s 50+ hours of technical hands-on courses: 1. Neural Networks Zero to Hero (Karpathy) https://t.co/KgziFx2P9u From micro-gradients to nanoGPT, code-first all the way. 2. Stanford CS336 (2025): Language Modelling from Scratch https://t.co/0gtF0omYKJ A full-stack LLM bootcamp: data → training → serving → evaluation. 3. MIT 6.S191 (2025): Intro to Deep Learning https://t.co/MFHzKqN7eR Transformers, diffusion, and modern DL in under 2 hours. 4. CS25: Intro to Transformers with Karpathy https://t.co/yx9w9sWtEt Turns “Attention Is All You Need” into code you can actually deploy. 5. Stanford CS229 Guest Lecture: Building LLMs https://t.co/X1Gh2ZBiny Behind the curtain of Stanford’s 2025 LLM stack. 6. Deep Dive into LLMs like ChatGPT https://t.co/1PxKRE0vax 3.5 hours of how GPTs really work under the hood. 7. Let’s Build GPT from Scratch https://t.co/r5UOw7584X 200 lines of Python → a functional GPT. Watch, code, repeat. 8. Agentic AI by Stanford https://t.co/GCimNjaT75 Gain an introduction to the concept of agentic AI language. 9. Transformers and Self-Attention https://t.co/pcZEYd9NNS Introduction to the Transformers architecture from scratch

9 AI & ML youtube videos you can’t miss as an AI engineer.

Fri, 11 Sep 2026 22:44:01 GMT

9 AI & ML youtube videos you can’t miss as an AI engineer.

Fri, 11 Sep 2026 22:44:01 GMT

9 AI & ML youtube videos you can’t miss as an AI engineer.

Fri, 11 Sep 2026 22:44:01 GMT

9 AI & ML youtube videos you can’t miss as an AI engineer.

Fri, 11 Sep 2026 22:44:01 GMT

🧠📚 Beyond the empirical successes—what do neural networks actually do, and how can we understand them through the lenses of statistics, information theory, and Gaussian processes?

Fri, 11 Sep 2026 22:44:01 GMT

🧠📚 Beyond the empirical successes—what do neural networks actually do, and how can we understand them through the lenses of statistics, information theory, and Gaussian processes? 📘 𝙇𝙚𝙘𝙩𝙪𝙧𝙚 𝙉𝙤𝙩𝙚𝙨 𝙞𝙣 𝘿𝙚𝙚𝙥 𝙇𝙚𝙖𝙧𝙣𝙞𝙣𝙜: 𝙏𝙝𝙚𝙤𝙧𝙚𝙩𝙞𝙘𝙖𝙡 𝙄𝙣𝙨𝙞𝙜𝙝𝙩𝙨 𝙞𝙣𝙩𝙤 𝙖𝙣 𝘼𝙧𝙩𝙞𝙛𝙞𝙘𝙞𝙖𝙡 𝙈𝙞𝙣𝙙 by Shlomo Dubnov and Dongmian Zou offers a much-needed theoretical introduction to deep learning—from foundational neural network modelling and optimisation to advanced topics like neural tangent kernels, Gaussian processes, and information theory. Designed to be accessible to upper-level undergraduates yet rigorous enough for advanced readers, this volume bridges intuition and formalism—relating deep learning to broader statistical and information modelling approaches. 🔎 𝐖𝐡𝐲 𝐭𝐡𝐢𝐬 𝐛𝐨𝐨𝐤 𝐢𝐬 𝐞𝐬𝐬𝐞𝐧𝐭𝐢𝐚𝐥 𝐫𝐞𝐚𝐝𝐢𝐧𝐠: 📖 1. Neural Network Foundations 🧱 Introduction to neural networks and their basic principles 🔧 Neural networks in use across practical applications ⚙️ Core optimisation methods underpinning training 🪞 2. Representation Learning 📐 Autoencoders and principal components analysis 🎲 Probabilistic PCA and variational autoencoders 🔁 Latent representations and dimensionality reduction 🖼️ 3. Architectures: CNNs, RNNs, and Transformers 🎨 Convolutional neural networks and their vision/audio applications ⏳ Recurrent neural networks for sequential data 🔍 Attention mechanisms and Transformer architectures 🎭 4. Generative Models ⚔️ Generative Adversarial Networks (GANs) and Wasserstein GANs 🌊 Normalising flows for tractable generative modelling 💎 Diffusion models for high-quality generation 🧮 5. Theoretical Foundations and Advanced Topics 📊 Information theory of learning 🌀 Neural networks as Gaussian processes 🔬 Transfer learning, explainable AI, and deep reinforcement learning 🌐 Explore the book here: https://t.co/hIMIqTkBZf 💡 Ideal for researchers, professionals, academics, and undergraduate and graduate students in artificial intelligence, machine learning, and information sciences—especially those seeking the theoretical foundations behind today's deep learning successes. 👉 Quote 𝐖𝐒𝐓𝐖𝐓𝐑𝟑𝟎 at checkout to enjoy 𝟑𝟎% 𝐨𝐟𝐟 your purchase now! #DeepLearningTheory #NeuralTangentKernel #NeuralNetworksAsGaussianProcesses #InformationTheoryOfLearning #VariationalAutoencoders #GenerativeAdversarialNetworks #WassersteinGAN #NormalisingFlows #DiffusionModels #TransformerArchitectures #AttentionMechanisms #ConvolutionalNeuralNetworks #RecurrentNeuralNetworks #RepresentationLearning #ExplainableAI #TransferLearning #DeepReinforcementLearning #PrincipalComponentsAnalysis

Hugging Face Daily Papers — 2026-09-09

Fri, 11 Sep 2026 22:44:01 GMT

Hugging Face Daily Papers — 2026-09-09 47 papers today. Full list with one-line highlights and arXiv links: 1. NeoHorse-1: Towards Recursive Self-Improvement via Agentic Post-Training with Routing Harness — Recursive self-improvement (RSI) requires a concrete mechanism through which an AI system observes its capabilities and converts... https://t.co/0Jn5vmxwJ8 2. AuK Technical Report: An Open-Source Foundational Model for Speech Generation and Editing — We introduce AuK, an open-source foundational model that unifies speech generation and editing through a common interface of natu... https://t.co/peaxKKh1M8 3. Omni Interaction Agent Technical Report — In this work, we present Gander, an end-to-end model that unifies omni perception, realtime interaction, and agentic capabilities... https://t.co/EllCN4Zn8U 4. Eliciting Weak-to-Strong Generalization with On-Policy Reverse Distillation — Weak-to-strong generalization asks whether stronger models can learn from weaker supervisors and surpass them. This question is p... https://t.co/ikrR0D2Lq5 5. DriveZero: End-to-End Driving Beyond Human Demonstrations — Most end-to-end autonomous-driving systems learn by imitating human driving logs, leaving their learned behavior constrained by t... https://t.co/FC7x47hB77 6. OpenWAM: An Open, Modular Exploration Towards Systematic World-Action Model Pretraining — World-Action Models inherit world knowledge from video-generative priors, and channel it into executable control signals through... https://t.co/QztDh7LQ9A 7. GE-Act 2.0: Pretraining and Scaling a World-Action Model for Robotic Manipulation — World-action models (WAM) predict future states to guide robot actions, enabling learning from both action-free video and action-... https://t.co/fNDzzjnn5D 8. Marigold V2: Revisiting Diffusion Transformers for Monocular Depth Estimation — Monocular depth estimation is a ubiquitous yet highly ill-posed computer vision task, with downstream applications in scene recon... https://t.co/tA7ADqtOSb 9. Miles v0.1: Production-Level Post-Training — We present Miles v0.1, a full-stack, production-ready system for frontier post-training. Building upon the clean design of slime... https://t.co/K9vnh4XVg1 10. Mask Forcing: Improving Autoregressive Video Diffusion Distillation via Dual-Noise Masking Rollout — Autoregressive (AR) video diffusion models have shown great potential in real-time video generation. Recent methods distill pretr... https://t.co/5Rs67oQgh7 11. SceneMosaic: Efficient and Diverse Simulation-Ready Scene Generation via Hybrid Agentic Layout Evolution — Diverse and simulation-ready indoor scenes are essential for interactive entertainment and embodied AI, yet their scalable genera... https://t.co/lUubNbflar 12. BeaconKV: Key-Value Cache Compression Guided by Beacon Queries for Efficient Large Reasoning Model Inference — Large Reasoning Models (LRMs) achieve superior problem-solving through extended Chain-of-Thought (CoT) generation, but the result... https://t.co/8JbkrFSMyw 13. Reason Through the Latent! Making Latent Visual Reasoning Necessary — Latent visual reasoning aims to perform multimodal reasoning through hidden-state computation rather than explicit textual chains... https://t.co/VHWKgctyB5 14. Steering Geometry: Validating Human Value Geometry in LLM Steering Space — As large language models (LLMs) are increasingly deployed in alignment-sensitive contexts, activation steering has emerged as a l... https://t.co/iyX3tkXNDp 15. Kalman Delta Networks: Uncertainty-aware Associative Memory — Linear attention is increasingly used in frontier language models for efficient long-context inference and constant-memory decodi... https://t.co/1OVvkFzGsr 16. CosmoH2G: A Hand-to-Gripper Transfer Dataset and Baseline Method for Object Manipulation with Complex Spatial Movements — Transferring human hand demonstrations to robotic grippers has recently emerged as a cost-effective solution for robot learning.... https://t.co/SGhhHeH5pU 17. Online Draft Co-Training for Speculative Decoding in Large-Scale, Long-Context RL Post-Training — Speculative decoding accelerates rollout generation, which dominates the cost of reinforcement learning (RL) post-training. Onlin... https://t.co/CoT6siYXb8 18. Agentic Visual Generation: From Generative Models to Agentic Control — Visual generation is evolving from generative models used through a single invocation into agentic control processes that can pla... https://t.co/Joxkr8nySa 19. Procedural Graphs: Self-Evolving Execution Structures for LLM Agents — Large language models are increasingly deployed as agents that plan over long horizons and act through external tools. Most agent... https://t.co/fy7FGubcgN 20. Environments as Scaffold: Enriching Feedback to Bootstrap Self-Evolving Agents in Long-Horizon Tasks — Large Language Models demonstrate remarkable proficiency in static reasoning, yet training them as autonomous agents through Rein... https://t.co/IslxbXV079 21. CoVeR: Coverage-Based Token Pruning for Multi-View 3D Reasoning in VLMs — Representing a 3D scene as multi-view images allows 2D VLMs to reason in 3D by reusing priors from pre-training, sidestepping the... https://t.co/3nyjqNbM95 22. RoboSPA: Can VLA Models Go Beyond Simple Scenes and Short-Horizon Tasks? — Vision-Language-Action (VLA) models have shown promising progress in language-conditioned robotic manipulation. However, existing... https://t.co/MkXuSpSSjD 23. TANGO: Humanoid Navigation in Cluttered Environments with a Whole-Body Vision-Language-Action Model — We study the problem of navigating cluttered indoor environments with a humanoid robot. Unlike conventional methods that model na... https://t.co/PtgIe14RfI 24. TransNormal-2: Geometry-Grounded Rectified Flow with Edge-Aware Decoding for Precise Normal Estimation — Diffusion-based models enable monocular geometry estimation, yet their pixel-space precision is limited by a shared, under-studie... https://t.co/5C2sIJgsTo 25. What LLM Trading Agents Actually Do in Production: A Six-Month, Population-Scale Record from Two Fleets — We present a continuous, population-scale measurement record of autonomous language-model trading agents operating in production... https://t.co/xPhBuQvaz9 26. Measuring Language Transfer in Robot Policies: Adding Greek to a Cosmos3 Vision-Language-Action Policy — Robot foundation models are trained and evaluated predominantly in English, and robot demonstration corpora do not exist for most... https://t.co/m8Li62oUIl 27. VidaForge: Open Research Infrastructure for Video Pretraining Data Recipes — Video foundation models increasingly rely on large-scale pretraining data, yet the end-to-end data pipelines behind them remain l... https://t.co/R18ZkzzlkB 28. What Did I Just Say? Self-Listening for Full-Duplex Speech Models — Full-duplex spoken language models can listen and speak simultaneously, enabling them to handle interruptions and backchannels in... https://t.co/m4q4oBqYID 29. SynthGait-19K: A Physically Grounded Synthetic Video Dataset for Gait Parameter Estimation — Accurate estimation of clinically meaningful gait parameters from monocular video is important for scalable mobility assessment... https://t.co/YN4J1ULaSq 30. Harnessing CLIP and DINO: An Uncertainty-Aware Cascaded Fusion Network for Generalizable Deepfake Image Detection — The growing realism and accessibility of manipulated and generated faces threaten the trustworthiness of digital media. To detect... https://t.co/pmtw08vL08 31. SQS: Bayesian DNN Compression through Sparse Quantized Sub-distributions — Compressing large-scale neural networks is essential for deploying models on resource-constrained devices. Most existing methods... https://t.co/e4rx81jq48 32. ReactVAU: A Slow-Fast Decoupled Framework for Streaming Video Anomaly Understanding — In this paper, we propose ReactVAU, a Slow-Fast Decoupled Framework for real-time streaming Video Anomaly Understanding (VAU). Ex... https://t.co/Amkf06z4Pp 33. MOLE: Detecting Insider Threats in AI Agents — Model misalignment, prompt injection, or operator misuse could lead AI agents operating frontier-lab accounts to exfiltrate model... https://t.co/luJq7EaDCu 34. Encoded Early, Used Late: Where Transformers Begin to Act on an Inferred Partner's Expertise — A transformer can make an attribute linearly decodable in its residual stream at a depth where that attribute does not yet influe... https://t.co/6FZDpJPTUj 35. Multi-Grid Post-Training for Long-Form Multi-Shot Video Generation — Generating long-form multi-shot videos requires coherent within-shot motion and visually consistent narratives across shots. Exis... https://t.co/SMEGfbeebZ 36. Cadence: Error-Bounded Lossy Compression of Demand Time Series with a Time-Series Foundation Model — Highlights 方法框架与实验验证结合 with a new benchmark, system, or framework direction. https://t.co/abpYPoG2Sn 37. Recognition-Refusal Misalignment in LLMs: Why Models Answer Structurally Unanswerable Questions — Large language models often answer structurally unanswerable questions, such as computing cot(-540°) or evaluating (1).startswith... https://t.co/SDaS3uimcN 38. A*-Thought-V2: Efficient Latent Reasoning via Geometric Dynamics of LLM — Highlights 推理能力增强, 潜空间建模 with a new benchmark, system, or framework direction. https://t.co/xRK7hoQbBU 39. VDiff-Bench: A Challenging Benchmark for Fine-Grained Image Difference Identification — Highlights 方法框架与实验验证结合 with a new benchmark, system, or framework direction. https://t.co/Xqp5bqUi12 40. RelightFormer: Feed-forward Generative Transformer for Multiview Object Relighting — Highlights 方法框架与实验验证结合 with a new benchmark, system, or framework direction. https://t.co/f1ugvKxdb3 41. NOAH: Learning the Full Patient Journey. A Longitudinal Multimodal Time-Aware Model for Representation and Forecasting — The digitization of healthcare has generated vast, longitudinal, and multimodal patient records over a lifetime, yet fully exploi... https://t.co/QRb9X6ZxSE 42. Counte

Hugging Face Daily Papers — 2026-09-09

Fri, 11 Sep 2026 22:44:01 GMT

Hugging Face Daily Papers — 2026-09-09

Fri, 11 Sep 2026 22:44:01 GMT

Hugging Face Daily Papers — 2026-09-09

Fri, 11 Sep 2026 22:44:01 GMT

Hugging Face Daily Papers — 2026-09-09

Fri, 11 Sep 2026 22:44:01 GMT

9 AI & ML youtube videos you can’t miss as an AI engineer.

Fri, 11 Sep 2026 22:44:01 GMT

9 AI & ML youtube videos you can’t miss as an AI engineer. It’s 50+ hours of technical hands-on courses: 1. Neural Networks Zero to Hero (Karpathy) https://t.co/XNzJuHc6FJ From micro-gradients to nanoGPT, code-first all the way. 2. Stanford CS336 (2025): Language Modelling from Scratch https://t.co/POJxV3Dysg A full-stack LLM bootcamp: data → training → serving → evaluation. 3. MIT 6.S191 (2025): Intro to Deep Learning https://t.co/U5ReE5RzZr Transformers, diffusion, and modern DL in under 2 hours. 4. CS25: Intro to Transformers with Karpathy https://t.co/nZz4DcvgAb Turns “Attention Is All You Need” into code you can actually deploy. 5. Stanford CS229 Guest Lecture: Building LLMs https://t.co/lGCD1S7MX2 Behind the curtain of Stanford’s 2025 LLM stack. 6. Deep Dive into LLMs like ChatGPT https://t.co/sbnqZTk5iu 3.5 hours of how GPTs really work under the hood. 7. Let’s Build GPT from Scratch https://t.co/1y7LEY3Rxk 200 lines of Python → a functional GPT. Watch, code, repeat. 8. Agentic AI by Stanford https://t.co/7KdN1C5qCM Gain an introduction to the concept of agentic AI language. 9. Transformers and Self-Attention https://t.co/Nn2TQUgUiC Introduction to the Transformers architecture from scratch

9 AI & ML youtube videos you can’t miss as an AI engineer.

Fri, 11 Sep 2026 22:44:01 GMT

9 AI & ML youtube videos you can’t miss as an AI engineer.

Fri, 11 Sep 2026 22:44:01 GMT

9 AI & ML youtube videos you can’t miss as an AI engineer.

Fri, 11 Sep 2026 22:44:01 GMT

9 AI & ML youtube videos you can’t miss as an AI engineer.

Fri, 11 Sep 2026 22:44:01 GMT

Complete Transformers For NLP Deep Learning One Shot With Handwritten Notes

Fri, 11 Sep 2026 22:36:41 GMT

The Transformer neural network is a powerful deep learning model that was introduced in a landmark paper titled "attention is all you need" by Vaswani et al. in 2017. It revolutionized the field of natural language processing (NLP) and has since found applications in various other domains. Handwritten Materials : https://github.com/krishnaik06/Transformers-Materials Timestamp 00:00:00 Introduction 00:04:07 What And Why Transformers 00:22:21 Basic Architecture Of Transformers 00:36:29 Self Attention Architecture 01:38:32 Multi Head Attention 01:48:46 Feed Forward With Multi Head Attention 01:57:24 Possition Encoding 02:27:27 Layer Normalization In Transformers 03:01:03 Complete Encoder Architecture 03:23:09 Layer Normalization Example 03:30:56 Decoder Plan Of Action 03:39:23 Decoder MAsked Attention head 04:32:36 Encoder And decoder Multi Head Attention 04:47:00 Linear And Softmax Layer ------------------------------------------------------------------------------------------- Check out all my udemy courses below ,the coupon code is valid for another 2 days more, this is the last coupon of the month. New Course Launched Complete MLOps Bootcamp With 10+ End To End ML Projects https://bit.ly/3Uf77Xj Complete Python With DSA Bootcamp + LEETCODE Exercises: https://bit.ly/3YvwBm0 Mathematics-Basics to Advanced for Data Science And GenAI : https://bit.ly/3YqF1e1 Complete Data Analyst Bootcamp From Basics To Advanced https://bit.ly/3Ybg4SF Complete Machine Learning NLP Bootcamp MLOPS And Deployment: https://bit.ly/3YbbJid Complete Generative AI Course With Langchain and Huggingface: https://bit.ly/3Yvx4EM Building GEN AI App 12+ Hands On Projects With Gemini Pro: https://bit.ly/4eJtmNt Interested In UI UX Design Mastering Figma from 0 to 100 (UI/UX Mastery Course) https://bit.ly/4h7SPla

The complete guide to Transformer neural Networks!

Fri, 11 Sep 2026 22:36:41 GMT

Let's do a deep dive into the Transformer Neural Network Architecture for language translation. ABOUT ME ⭕ Subscribe: https://www.youtube.com/c/CodeEmporium?sub_confirmation=1 📚 Medium Blog: https://medium.com/@dataemporium 💻 Github: https://github.com/ajhalthor 👔 LinkedIn: https://www.linkedin.com/in/ajay-halthor-477974bb/ RESOURCES [ 1 🔎] Transformer Architecture Image :https://github.com/ajhalthor/Transformer-Neural-Network/blob/main/Transformer_Architecture_complete.png [2 🔎] draw.io version of the image for clarity: https://github.com/ajhalthor/Transformer-Neural-Network/blob/main/Transformer_Architecture_complete.drawio PLAYLISTS FROM MY CHANNEL ⭕ Transformers from scratch playlist: https://www.youtube.com/watch?v=QCJQG4DuHT0&list=PLTl9hO2Oobd97qfWC40gOSU8C0iu0m2l4 ⭕ ChatGPT Playlist of all other videos: https://youtube.com/playlist?list=PLTl9hO2Oobd9coYT6XsTraTBo4pL1j4HJ ⭕ Transformer Neural Networks: https://youtube.com/playlist?list=PLTl9hO2Oobd_bzXUpzKMKA3liq2kj6LfE ⭕ Convolutional Neural Networks: https://youtube.com/playlist?list=PLTl9hO2Oobd9U0XHz62Lw6EgIMkQpfz74 ⭕ The Math You Should Know : https://youtube.com/playlist?list=PLTl9hO2Oobd-_5sGLnbgE8Poer1Xjzz4h ⭕ Probability Theory for Machine Learning: https://youtube.com/playlist?list=PLTl9hO2Oobd9bPcq0fj91Jgk_-h1H_W3V ⭕ Coding Machine Learning: https://youtube.com/playlist?list=PLTl9hO2Oobd82vcsOnvCNzxrZOlrz3RiD MATH COURSES (7 day free trial) 📕 Mathematics for Machine Learning: https://imp.i384100.net/MathML 📕 Calculus: https://imp.i384100.net/Calculus 📕 Statistics for Data Science: https://imp.i384100.net/AdvancedStatistics 📕 Bayesian Statistics: https://imp.i384100.net/BayesianStatistics 📕 Linear Algebra: https://imp.i384100.net/LinearAlgebra 📕 Probability: https://imp.i384100.net/Probability OTHER RELATED COURSES (7 day free trial) 📕 ⭐ Deep Learning Specialization: https://imp.i384100.net/Deep-Learning 📕 Python for Everybody: https://imp.i384100.net/python 📕 MLOps Course: https://imp.i384100.net/MLOps 📕 Natural Language Processing (NLP): https://imp.i384100.net/NLP 📕 Machine Learning in Production: https://imp.i384100.net/MLProduction 📕 Data Science Specialization: https://imp.i384100.net/DataScience 📕 Tensorflow: https://imp.i384100.net/Tensorflow TIMESTAMPS 0:00 Introduction 1:38 Transformer at a high level 4:15 Why Batch Data? Why Fixed Length Sequence? 6:13 Embeddings 7:00 Positional Encodings 7:58 Query, Key and Value vectors 9:19 Masked Multi Head Self Attention 14:46 Residual Connections 15:50 Layer Normalization 17:57 Decoder 20:12 Masked Multi Head Cross Attention 22:47 24:03 Tokenization & Generating the next translated word 26:00 Transformer Inference Example

MIT 6.S191 (2025): Recurrent Neural Networks, Transformers, and Attention

Fri, 11 Sep 2026 22:36:41 GMT

MIT Introduction to Deep Learning 6.S191: Lecture 2 Recurrent Neural Networks Lecturer: Ava Amini ** New 2025 Edition ** For all lectures, slides, and lab materials: http://introtodeeplearning.com Subscribe to stay up to date with new deep learning lectures at MIT, or follow us @MITDeepLearning on Twitter and Instagram to stay fully-connected!!

MIT 6.S191: Recurrent Neural Networks, Transformers, and Attention

Fri, 11 Sep 2026 22:36:41 GMT

MIT Introduction to Deep Learning 6.S191: Lecture 2 Recurrent Neural Networks Lecturer: Ava Amini ** New 2026 Edition ** For all lectures, slides, and lab materials: http://introtodeeplearning.com Subscribe to stay up to date with new deep learning lectures at MIT, or follow us @MITDeepLearning on Twitter and Instagram to stay fully-connected!!

Lec 08. Architectures: Transformers | Deep Learning

Fri, 11 Sep 2026 22:36:41 GMT

This video introduces transformers, focusing on three key ideas: tokens, attention, and positional codes. It also explores how transformers relate to MLPs, ...

Transformers Step-by-Step Explained (Attention Is All You Need)

Fri, 11 Sep 2026 22:36:41 GMT

Build better full-stack authentication and user management with Clerk: https://go.clerk.com/Q8BtT1n -- We just launched the all-in-one tech interview prep platform, covering coding, system design, OOD, and machine learning. Launch sale: 50% off. Check it out: https://bit.ly/bbg-yt

MIT 6.S191 (2024): Recurrent Neural Networks, Transformers, and Attention

Fri, 11 Sep 2026 22:36:41 GMT

MIT Introduction to Deep Learning 6.S191: Lecture 2 Recurrent Neural Networks Lecturer: Ava Amini 2024 Edition For all lectures, slides, and lab materials: http://introtodeeplearning.com Lecture Outline 0:00 - Introduction 3:42 - Sequence modeling 5:30 - Neurons with recurrence 12:20 - Recurrent neural networks 14:08 - RNN intuition 17:14 - Unfolding RNNs 19:54 - RNNs from scratch 22:41 - Design criteria for sequential modeling 24:24 - Word prediction example 31:50 - Backpropagation through time 33:40 - Gradient issues 37:15 - Long short term memory (LSTM) 40:00 - RNN applications 44:00- Attention fundamentals 46:46 - Intuition of attention 49:13 - Attention and search relationship 51:22 - Learning attention with neural networks 57:45 - Scaling attention and applications 1:00:08 - Summary Subscribe to stay up to date with new deep learning lectures at MIT, or follow us @MITDeepLearning on Twitter and Instagram to stay fully-connected!!

Transformer Neural Networks, ChatGPT's foundation, Clearly Explained!!!

Fri, 11 Sep 2026 22:36:41 GMT

Transformer Neural Networks are the heart of pretty much everything exciting in AI right now. ChatGPT, Google Translate and many other cool things, are based on Transformers. This StatQuest cuts through all the hype and shows you how a Transformer works, one-step-at-a time. NOTE: If you're interested in learning more about Backpropagation, check out these 'Quests: The Chain Rule: https://youtu.be/wl1myxrtQHQ Gradient Descent: https://youtu.be/sDv4f4s2SB8 Backpropagation Main Ideas: https://youtu.be/IN2XmBhILt4 Backpropagation Details Part 1: https://youtu.be/iyn2zdALii8 Backpropagation Details Part 2: https://youtu.be/GKZoOHXGcLo If you're interested in learning more about the SoftMax function, check out: https://youtu.be/KpKog-L9veg If you're interested in learning more about Word Embedding, check out: https://youtu.be/viZrOnJclY0 If you'd like to learn more about calculating similarities in the context of neural networks and the Dot Product, check out: Cosine Similarity: https://youtu.be/e9U0QAFbfLI Attention: https://youtu.be/PSs6nxngL6k For a complete index of all the StatQuest videos, check out: https://statquest.org/video-index/ If you'd like to support StatQuest, please consider... Patreon: https://www.patreon.com/statquest ...or... YouTube Membership: https://www.youtube.com/channel/UCtYLUTtgS3k1Fg4y5tAhLbw/join ...buying one of my books, a study guide, a t-shirt or hoodie, or a song from the StatQuest store... https://statquest.org/statquest-store/ ...or just donating to StatQuest! https://www.paypal.me/statquest Lastly, if you want to keep up with me as I research and create new StatQuests, follow me on twitter: https://twitter.com/joshuastarmer 0:00 Awesome song and introduction 1:26 Word Embedding 7:30 Positional Encoding 12:53 Self-Attention 23:37 Encoder and Decoder defined 23:53 Decoder Word Embedding 25:08 Decoder Positional Encoding 25:50 Transformers were designed for parallel computing 27:13 Decoder Self-Attention 27:59 Encoder-Decoder Attention 31:19 Decoding numbers into words 32:23 Decoding the second token 34:13 Extra stuff you can add to a Transformer #StatQuest #Transformer #ChatGPT

20 YouTube channels that teach AI better than most CS degrees in 2026:

Fri, 11 Sep 2026 22:29:21 GMT

20 YouTube channels that teach AI better than most CS degrees in 2026: 1. Andrej Karpathy Deep, intuitive walkthroughs of neural networks and modern LLMs https://t.co/8nHCOsDkvW 2. 3Blue1Brown Visual intuition for math, linear algebra, and neural networks https://t.co/jljtwCb97a 3. StatQuest with Josh Starmer Clear, friendly explanations of statistics and ML fundamentals https://t.co/u0HjJ8R4Nz 4. Stanford Online University-grade ML and AI lecture series (Andrew Ng, CS229, etc.) https://t.co/mvV6h3F6q3 5. sentdex Practical machine learning and Python projects https://t.co/ZwkatTeBrA 6. Yannic Kilcher Deep dives into ML and AI research papers https://t.co/geNgF8zfbO 7. MIT OpenCourseWare Rigorous academic courses on ML, AI, and applied mathematics https://t.co/piqcFXsME8 8. Siraj Raval High-level overviews and motivation around AI concepts Link: https://t.co/Cr4D8Q1zfN 9. DeepLearningAI Structured learning paths for deep learning and generative AI https://t.co/kADe5Azzn2 10. Two Minute Papers Fast, accessible summaries of cutting-edge AI research https://t.co/C8OjcxSuC4 11. Umar Jamil Clear, implementation-focused explanations of transformers and LLMs https://t.co/m1KUxYk0Pm 12. Hugging Face Open-source LLMs, transformers, and modern NLP tooling https://t.co/QNziUpruOw 13. Steve Brunton Dynamical systems, control theory, and scientific ML https://t.co/1xVaR2i9YR 14. Michael Bronstein Geometric deep learning and graph neural networks https://t.co/pUM7naSBYL 15. Caltech Advanced lecture series on ML, optimization, and theory https://t.co/L19OVfgxZ8 16. Lex Fridman Long-form conversations with top AI researchers and practitioners https://t.co/DealNqlGAu 17. Arxiv Insights Beginner-friendly explanations of recent AI papers https://t.co/s219lirIgy 18. Machine Learning Street Talk Unfiltered, technical discussions on AI research and theory https://t.co/AT6SFswbym 19. Jeremy Howard Practical deep learning with strong intuition https://t.co/kQFIPbM1uv 20. Kaggle Applied ML, competitions, notebooks, and real-world workflows https://t.co/QNj0Lg4UIE

20 YouTube channels that teach AI better than most CS degrees in 2026:

Fri, 11 Sep 2026 22:29:21 GMT

20 YouTube channels that teach AI better than most CS degrees in 2026:

Fri, 11 Sep 2026 22:29:21 GMT

20 YouTube channels that teach AI better than most CS degrees in 2026:

Fri, 11 Sep 2026 22:29:21 GMT

20 YouTube channels that teach AI better than most CS degrees in 2026:

Fri, 11 Sep 2026 22:29:21 GMT

Linear Algebra Intuition: Vectors & Matrices Explained Visually

Fri, 11 Sep 2026 22:22:01 GMT

Struggling to understand linear algebra? This video breaks down vectors and matrices using intuition and visuals, not heavy formulas. You’ll learn: What vectors really represent (direction & magnitude) How matrices act as space-transforming machines Why dot products measure similarity How linear algebra powers AI, machine learning, graphics, and neural networks This is an intuition-first explanation designed for developers, AI/ML beginners, data science learners, and interview prep. If you’ve ever memorized formulas without truly understanding them—this video is for you.

GEOMETRIC Interpretation of Vectors | Linear Algebra APPLICATIONS

Fri, 11 Sep 2026 22:22:01 GMT

In this video, we cover linear algebra applications. We connect algebraic and geometric interpretations of vectors. We show the geometric meaning of vector addition and scalar multiplication, and we consider what geometric shapes arise from random linear combinations of vectors. You may also be interested in my full linear algebra course: https://www.youtube.com/playlist?list=PLxQVx0jlffqfMhwn-i9q161gGxURlk7wb Students often ask is linear algebra important and is linear algebra hard. They wonder can anyone learn linear algebra and why some can't understand linear algebra. It is important to show to the students how linear algebra is used in data science, machine learning, AI, computer science, image processing, computer graphics, and in real life in general. This is a free video course in linear algebra for everyone, even beginners. It covers linear algebra with applications. #mathflipped

Foundations for ML | Simple intuition of eigenvalues and eigenvectors | Linear Algebra [Lecture 10]

Fri, 11 Sep 2026 22:22:01 GMT

"For Ax=λx, det(A-λI)=0. Sure, but why?" We all know that when Ax = λx, we solve det(A - λI) = 0 for λ to find the eigenvalues. Then we solve (A - λI)x=0 for x to find the eigenvectors. But what exactly are eigenvalues and eigenvectors? Geometrically, why should this condition det(A - λI) = 0 be true for λ being an eigenvalue? This lecture I published on Vizuara's YouTube channel covers a collection of ideas from linear algebra: https://youtu.be/-PmMvHPUto4 You will learn about (not just the math equations, but the actual intuition) 1) Linear transformations 2) Unit vector before and after transformation 3) The geometric intuition behind determinants (very cool stuff) 4) The intuition behind eigenvalues and eigenvectors This lecture is part of the "Foundations for ML course". Learn in detail from my new lecture. My goal is to lay a strong foundation for you in ML: https://youtu.be/-PmMvHPUto4

[Linear Algebra] Geometric Transformations

Fri, 11 Sep 2026 22:22:01 GMT

Geometric Transformations in linear algebra. Visit our website: http://bit.ly/1zBPlvm Subscribe on YouTube: http://bit.ly/1vWiRxW Like us on Facebook: http://on.fb.me/1vWwDRc Submit your questions on Reddit: http://bit.ly/1GwZZrP #LinearAlgebra #Algebra #UniversityMath #Lecture *--Playlists--* Linear Algebra: https://www.youtube.com/playlist?list=PLDDGPdw7e6AjJacaEe9awozSaOou-NIx_ *--Recommended Textbooks--* Linear Algebra and Its Applications (Lay): https://amzn.to/37gBZ27 Linear Algebra Done Right (Axler): https://amzn.to/2T0GpBI Introduction to Linear Algebra (Strang): https://amzn.to/3dC6kJq Linear Algebra: Step by Step (Singh): https://amzn.to/2T33G65 3,000 Solved Problems in Linear Algebra (Lipschutz): https://amzn.to/3j2nJMw We look at geometric transformations, so reflecting, shearing, compressing, expanding, and projecting. Hello, welcome to TheTrevTutor. I'm here to help you learn your college courses in an easy, efficient manner. If you like what you see, feel free to subscribe and follow me for updates. If you have any questions, leave them below. I try to answer as many questions as possible. If something isn't quite clear or needs more explanation, I can easily make additional videos to satisfy your need for knowledge and understanding.

Dot products and duality | Chapter 9, Essence of linear algebra

Fri, 11 Sep 2026 22:22:01 GMT

Why the formula for dot products matches their geometric intuition. Help fund future projects: https://www.patreon.com/3blue1brown An ...

Inverse matrices, column space and null space | Chapter 7, Essence of linear algebra

Fri, 11 Sep 2026 22:22:01 GMT

How to think about linear systems of equations geometrically. Help fund future projects: https://www.patreon.com/3blue1brown An equally valuable form of support is to simply share some of the videos. Home page: https://www.3blue1brown.com/ Full series: http://3b1b.co/eola Future series like this are funded by the community, through Patreon, where supporters get early access as the series is being produced. http://3b1b.co/support Thanks to these viewers for their contributions to translations Hebrew: Omer Tuchfeld ------------------ 3blue1brown is a channel about animating math, in all senses of the word animate. And you know the drill with YouTube, if you want to stay posted about new videos, subscribe, and click the bell to receive notifications (if you're into that). If you are new to this channel and want to see more, a good place to start is this playlist: https://goo.gl/WmnCQZ Various social media stuffs: Website: https://www.3blue1brown.com Twitter: https://twitter.com/3Blue1Brown Patreon: https://patreon.com/3blue1brown Facebook: https://www.facebook.com/3blue1brown Reddit: https://www.reddit.com/r/3Blue1Brown

Introduction to Linear Algebra | Geometric Linear Algebra 1 | NJ Wildberger

Fri, 11 Sep 2026 22:22:01 GMT

This is the full first lecture of a course on Linear Algebra. Given by N J Wildberger of the School of Mathematics and Statistics at UNSW, the course gives a more geometric and natural approach to this important subject, with lots of interesting applications. Our orientation is that Linear Algebra is really ``Linear Algebraic Geometry'': so teaching the algebra without the geometry is depriving the student of the heart of the subject. The first lecture discusses the affine grid plane and introduces vectors, along with the number one problem of linear algebra: how to invert a linear change of coordinates! Intended audience: first year college or undergraduate students, motivated high school students, high school teachers, general public interested in mathematics. Enjoy! Video Contents (thanks to Lucas Lofaro) 0:00 Intro 0:07 Course Overview 2:08 What is Linear Algebra 4:51 Two Dimensional Affine Grid Plane 9:26 Vectors and Coordinates 13:52 Refining the Grid Plane 17:33 Essential Vector Operations 22:32 Describing Vectors with Two Different Coordinate Systems 27:11 Generalizing Coordinate Transformations 30:46 Main problem of Linear Algebra 34:01 General Solution in One and Two Dimensions 40:30 Outstanding Questions 41:06 Exercises ******* Research Gate page: https://www.researchgate.net/profile/Norman_Wildberger Blog: http://njwildberger.com/ Online courses at openlearning.com (currently Algebraic Calculus One): https://www.openlearning.com/courses/algebraic-calculus-one/ Please join us for an exciting new approach to one of mathematics' most important subjects! Patreon: https://www.patreon.com/njwildberger Your support would be much appreciated. Wild Egg Maths YT channel: https://www.youtube.com/channel/UCriFv3G22iOUidUhkIGXuh Insights into Mathematics Playlists: https://www.youtube.com/playlist?list=PLIljB45xT85CL0-5wO4P-OwuH7SzBUyC0 (31 videos) https://www.youtube.com/playlist?list=PLIljB45xT85B0aMG-G9oqj-NPIuBMnq8z (18 videos) https://www.youtube.com/playlist?list=PLIljB45xT85DHcleY93bd2WlywQYxHv7H (6 videos) https://www.youtube.com/playlist?list=PLIljB45xT85AZjh2GUdwsMuAWTEHXCLb- (4 videos) https://www.youtube.com/playlist?list=PLIljB45xT85DeX148Q2T2elycnIYHTxdP (44 videos) https://www.youtube.com/playlist?list=PLIljB45xT85A6D0eylgAM4Xc1b_dDTgHW (8 videos) https://www.youtube.com/playlist?list=PLIljB45xT85DGxj1x_dyaSggbauAgrB6R (226 videos) https://www.youtube.com/playlist?list=PLIljB45xT85A_VZD126sND3XECDSI-qGs (26 videos) https://www.youtube.com/playlist?list=PLIljB45xT85DfokRNdx5eQ_qA43BwyM-_ (45 videos) https://www.youtube.com/playlist?list=PLIljB45xT85CvrEUnhS4dkTm9Lci5JTYS (43 videos) https://www.youtube.com/playlist?list=PLIljB45xT85D7wczwyUQdwDe2duZ7wPTf (40 videos) https://www.youtube.com/playlist?list=PLIljB45xT85Ar_tuHmRDI2Xr5tWOTI1oW (55 videos) https://www.youtube.com/playlist?list=PLIljB45xT85DPSrQpExd2L870ggxd2KEB (34 videos) https://www.youtube.com/playlist?list=PLIljB45xT85C3-5f6-qQS3YhgygZgnGYs (8 videos) https://www.youtube.com/playlist?list=PLIljB45xT85D7hfVLKyEJbQxOJ4yYm1-9 (9 videos) https://www.youtube.com/playlist?list=PLIljB45xT85BtiJIcDF2uuFc7_ndd1UL6 ( 46 videos) https://www.youtube.com/playlist?list=PLIljB45xT85B6MYBrWldlJ3w-Q1LaEdoy (40 videos) https://www.youtube.com/playlist?list=PLIljB45xT85DIpzaWggqJDIK8m2u09dQL (7 videos) https://www.youtube.com/playlist?list=PLIljB45xT85Ag6ragdmHaxUOKcQsIsrXZ (7 videos) https://www.youtube.com/playlist?list=PLIljB45xT85A5w_vZ5BB1IQAxAMhz5o5S (19 videos) https://www.youtube.com/playlist?list=PLIljB45xT85BjhYV7QswANMF3gKPx9BqT (21 videos) https://www.youtube.com/playlist?list=PLIljB45xT85Bi7PvrTkAaAswTwsfsR5HV (10 videos) https://www.youtube.com/playlist?list=PLIljB45xT85CyF_7bKd6y36VArOy3p2oh (94 videos) Wild Egg Maths Playlists: https://www.youtube.com/playlist?list=PLzdiPTrEWyz5m3XKK0OPc31l-GTqDiAjs (4 videos) https://www.youtube.com/playlist?list=PLzdiPTrEWyz4CVqTYS1fwInPOZxBxDaQf (64 videos) https://www.youtube.com/playlist?list=PLzdiPTrEWyz7PpsRFHuGb3EhwZtEOdRjV (45 videos) https://www.youtube.com/playlist?list=PLzdiPTrEWyz6HqaFPB4HTZOQ8jgDAVnuz (20 videos) https://www.youtube.com/playlist?list=PLzdiPTrEWyz71VHhAIUN-7Y6-JVcwF-xA (8 videos) https://www.youtube.com/playlist?list=PLzdiPTrEWyz75-t4UaYvHwPe5AHGA_EWe (9 videos) https://www.youtube.com/playlist?list=PLzdiPTrEWyz5W25vaXSreTMTwpPhWs1w2 (52 videos) https://www.youtube.com/playlist?list=PLzdiPTrEWyz4DCenuIAr9R2kXLq4ywRAm (9 videos) https://www.youtube.com/playlist?list=PLzdiPTrEWyz7OynfQubQrYkxYurauO-HD (14 videos) https://www.youtube.com/playlist?list=PLzdiPTrEWyz7cyg5JdKBpzB4fTQT7f-Xl (8 videos) https://www.youtube.com/playlist?list=PLzdiPTrEWyz5Bcq1VN_R7zYDZH3u49FH8 (25 videos) https://www.youtube.com/playlist?list=PLzdiPTrEWyz5flY1TFd7cekRMZRvPGkzM (30 videos)

An Intuitive Introduction to Projective Geometry Using Linear Algebra

Fri, 11 Sep 2026 22:22:01 GMT

This is an area of math that I've wanted to talk about for a long time, especially since I have found how projective geometry can be used to formulate Euclidean, spherical, and hyperbolic geometries, and a possible (and hopefully plausible) way projective geometry (specifically the model that uses lines, planes, etc. through the origin) could have been discovered and not just created out of thin air. I am most likely not the first person to discover what I say in this video, but I have not found any sources that explicitly state the same things (except possibly NJ Wildberger with his video on how hyperbolic geometry is "projective relativistic geometry", which I haven't watched, but judging from the thumbnail it seems like he found the same connection between projective geometry and the Minkowski model of hyperbolic geometry that I make in this video). The first half of this video is intended for everyone; the second half (where I start talking about linear algebra) is intended for those who already know that subject on an introductory level, e.g. those who have taken a class in it or have watched 3Blue1Brown's series on it. Everything in this video comes from bits and pieces of articles and videos that I have sporadically watched over the last several (maybe 6 or 7) years, plus linear algebra that I have learned in a class I took more recently. As a result, I probably cannot give a complete list of all the sources I have used, but I will list as many as I can remember down below: Projective geometry: https://en.wikipedia.org/wiki/Homogeneous_coordinates https://www.youtube.com/watch?v=q3turHmOWq4 ("Projective geometry and homogeneous coordinates | WildTrig: Intro to Rational Trigonometry", Insights into Mathematics) Spherical geometry: https://en.wikipedia.org/wiki/Spherical_geometry https://brilliant.org/wiki/spherical-geometry/ Hyperbolic geometry: https://en.wikipedia.org/wiki/Hyperboloid_model https://www.youtube.com/watch?v=KO5eE5d59vQ ("Projection from Hyperboloid to the Beltrami–Klein disk.", Jamnitzer) https://dl.tufts.edu/concern/pdfs/bk128p14r ("Hyperbolic Geometry on a Hyperboloid", William F. Reynolds) https://www.roguetemple.com/z/hyper/models.php ("Models and projections of hyperbolic geometry", Rogue Temple) 2D and 3D plots were made with Desmos and GeoGebra, respectively. All other images were made by me in Google Slides. Chapters: PART 1 0:00 Intro 0:31 Defining projective points and lines 4:19 Spatial coordinates 7:11 Projective quadratics 8:40 Non-Euclidean geometries 10:52 Distance metrics 12:11 PART 2 (linear algebra) 12:33 Defining projective points, lines with linear algebra 13:47 clmspace vs. nullspace representation of projective linear objects (points, lines, planes, ...) 16:32 clmspace to nullspace representation of a projective line (includes cross product) 20:31 Spans of clmspaces and interseections of nullspaces 21:33 3D projective geometry 23:13 Projective quadratics and double-cones 26:34 Summary #SoME2

From an Economics without Capitalism to Markets without Capitalism – Tübingen University talk. A lecture organised by University of Tübingen economics students https://t.co/nUNJHA86aL

Fri, 11 Sep 2026 22:14:41 GMT

From an Economics without Capitalism to Markets without Capitalism – Tübingen University talk. A lecture organised by University of Tübingen economics students https://t.co/nUNJHA86aL

"Higher pork prices are expected to visibly impact overall inflation trends."

Fri, 11 Sep 2026 22:14:41 GMT

"Higher pork prices are expected to visibly impact overall inflation trends." Still remember attending my first economics lecture at Peking University, professor went on about pork prices for ~2 hours, I had no idea what was going on https://t.co/TIIWPqvMjQ

First year taster lecture from the School of Economics - University of Bristol

Fri, 11 Sep 2026 22:07:21 GMT

Dr Steven Proud, Director of Undergraduate studies at the University of Bristol offers a taste of what it would be like to study Economics. Find out more: https://www.bristol.ac.uk/economics

2018-19 Marshall Lecture Day 1 - Professor Robert J. Shiller

Fri, 11 Sep 2026 22:07:21 GMT

Professor Robert J. Shiller (Yale), gives lecture 1 of the 2018-19 Marshall Lecture on "Economic Narratives". This event took place on 14th November in Lady Mitchell Hall, Cambridge. Find out more about the Marshall Lecture series on the link below. https://www.econ.cam.ac.uk/Marshall_Lecture/ML-background.html

Economics at Warwick | Mini Taster Lecture with Dr Amrita Kulka

Fri, 11 Sep 2026 22:07:21 GMT

Experience a taste of studying as an economics student at Warwick by watching our taster video with Dr Amrita Kulka, Assistant Professor at the University of Warwick. The information provided in the talk was correct at the time of recording in June 2022. Our course, module content and schedule are continually reviewed and updated to reflect the latest research expertise at Warwick, so it is therefore very important that you check the relevant course website for the latest information before you apply and when you accept an offer. For full terms and conditions, please visit: warwick.ac.uk/ugtermsandconditions

2018-19 Marshall Lecture Day 2 - Professor Robert J. Shiller

Fri, 11 Sep 2026 22:07:21 GMT

Professor Robert J. Shiller (Yale), gives lecture 2 of the 2018-19 Marshall Lecture on "Economic Narratives". This event took place on 15th November in Lady Mitchell Hall, Cambridge. Find out more about the Marshall Lecture series on the link below. https://www.econ.cam.ac.uk/Marshall_Lecture/ML-background.html

Warning: This college course may be bad for your brain | Owen Anderson, Blaze Media

Fri, 11 Sep 2026 22:00:01 GMT

Warning: This college course may be bad for your brain | Owen Anderson, Blaze Media An ‘intellectual nutrition label’ could warn students when a professor’s public record points toward indoctrination rather than intellectual inquiry. Every August, families spend tens of thousands of dollars on college tuition, housing, meal plans, and textbooks. They compare degree requirements, graduation rates, and campus amenities. Yet many never ask the most basic consumer question: What is actually inside the classes they are buying? Colleges already label the price. It’s time to label the product. Americans would not buy food without reading the ingredients or take medicine without knowing something about the possible side effects. College courses, however, are often sold by title and catalog description alone. Students register for “Introduction to History,” “Ethics,” or even a science requirement with little idea of the intellectual assumptions the professor will bring into the lecture hall. That information can be consequential. A course may pursue knowledge through evidence and argument. It may also subordinate the subject to diversity, equity, and inclusion programs, critical theory, gender ideology, Marxist assumptions, or some other political project. Ideological activism is not confined to philosophy, literature, or religious studies. It increasingly appears across the curriculum, including in professional programs and “hard” scientific fields. Parents and students have every right to know whether a professor intends to teach the advertised subject, examine competing arguments, and permit disagreement — or use the classroom to promote a political worldview. The concern is especially urgent at public universities. Faculty members at state institutions are government employees paid by taxpayers. In Arizona, for example, they undertake obligations to uphold the United States and state constitutions. Taxpayers are therefore entitled to ask whether professors are educating students for citizenship in a constitutional republic or teaching theories fundamentally hostile to it. That’s why I developed what I call the “Intellectual Nutrition Label.” The idea is simple. A nutrition label does not order shoppers to buy or reject a product. It tells them what the product contains so they can decide for themselves. The Intellectual Nutrition Label would do the same for college professors and courses. Using public information — university biographies, curricula vitae, books, articles, interviews, lectures, and course materials — each label would summarize the intellectual content students are likely to encounter. Families should not need graduate training or weeks of research to understand what a professor openly teaches. A useful label might ask: - Does the professor encourage the pursuit of knowledge or deny that objective truth exists? - Does the course cultivate virtue and self-command or treat desire as its own justification? - Does it direct students toward goodness, beauty, and wisdom or toward resentment, envy, and contempt? - Does the professor teach students how to examine arguments or tell them which political conclusions a decent person must reach? A label could also rate qualities such as intellectual honesty, knowledge, virtue, beauty, meaningfulness, and piety. A professor might rank highly for candor and scholarship while receiving a low rating for piety because he publicly rejects belief in God. The point would not be to uncover private opinions or publish anonymous accusations. The ratings would summarize what professors already say about themselves and their work. That distinction is essential. An Intellectual Nutrition Label should not become a blacklist, an invitation to harassment, or a substitute for reading a professor’s work. It must rely on verifiable public evidence, represent a professor’s position fairly, and provide enough sourcing for readers to check the judgment for themselves. The concept has predecessors. David Horowitz’s 2006 book, “The Professors: The 101 Most Dangerous Academics in America,” documented ideological activism in higher education. The David Horowitz Freedom Center continues to track left-wing activists and organizations. Turning Point USA’s Professor Watchlist sought to alert students to professors who brought political activism into the classroom and briefly maintained a list of recommended faculty. The Intellectual Nutrition Label would have a narrower purpose: consumer disclosure. It would tell students and parents what intellectual ingredients a professor publicly advertises, in a format ordinary people can understand before registration day. If a professor teaches that the United States is inherently oppressive, advocates Marxist critical theory, rejects biological sex as fixed, or treats Christianity principally as an instrument of domination, those positions should be documented in the professor’s own publications, biography, lectures, or course materials. An accurate summary of publicly stated beliefs is not censorship. The more serious objection may be that parents and taxpayers were never expected to notice. Academic language often conceals radical assumptions behind specialized vocabulary. Professors can signal their commitments to colleagues while students discover the course’s real content only after enrollment. That arrangement is losing its protection. Parents increasingly ask what their children are learning. Taxpayers increasingly ask whether public universities are educating students or recruiting them into political movements. Those are legitimate questions, especially when a single semester can cost more than many families earn in months. Throughout the coming semester, I will publish Intellectual Nutrition Labels evaluating professors from publicly available evidence. The labels will not decide which courses students may take. They will help families make that decision with their eyes open. Readers can follow the project on my Substack and at https://t.co/dWW6infYzZ (coming soon). When families spend tens of thousands of dollars on a college education, they deserve to know who is teaching, what that professor believes, and which intellectual ingredients will be placed before their children. Colleges already label the price. It’s time to label the product. https://t.co/qFk1rJ1MSe

Warning: This college course may be bad for your brain | Owen Anderson, Blaze Media

Fri, 11 Sep 2026 22:00:01 GMT

In 2025, reposition your career or business with AI (artificial intelligence). Why? In a Logic and Philosophy class (GST 103) as an undergraduate in Federal University of Technology Owerri (FUTO), Nigeria, my professor explained one of the most foundational postulations of Pythagoras: the universe i

Fri, 11 Sep 2026 22:00:01 GMT

In 2025, reposition your career or business with AI (artificial intelligence). Why? In a Logic and Philosophy class (GST 103) as an undergraduate in Federal University of Technology Owerri (FUTO), Nigeria, my professor explained one of the most foundational postulations of Pythagoras: the universe is numbers. Thales, Heraclitus and other philosophers had different explanations, ranging from water to fire. But the Pythagoras' explanation was supreme. That lecture that day reminded me what happened in my first course in Physics in senior secondary, when Mr. Aham introduced us to the study of matter in relation to energy, as a subset of Natural Philosophy, and linking all to mathematics, the beautiful science of numbers. Simply, logic rules the world and every knowledge converges in philosophy, which means if you get a PhD in Chemistry, you have simply mastered logic and philosophy (the PHD title) in chemistry! So, at the height of knowledge, it is all about philosophy. If we connect Pythagoras postulation and what AI (artificial intelligence) is doing today, we can see that AI is helping us to understand our world better, since if the world is made up of numbers, it does mean that the business of man and woman, is making sense of numbers. Hello, that is computing and now with intelligence. Is that not exciting? It has a promise to change the world, as calculating, processing, etc, has evolved from the age of abacus, slide rule, difference engine,...., to now AI. AI will change the world, and as the Igbo Nation says “uwa bu ahia” [the world is a marketplace], it does imply that AI will change the marketplace. In other words, AI will change careers and businesses. But AI is not just about coding: think of how workers transitioned to Microsoft Word from typewriters, and how knowing how to use wordpressors like Microsoft Word is expected at workplaces. Yes, understanding how to use specific AI tools for your job will be as important as using Word and Excel at work. If you have no knowledge of any, it may be seen as a weakness. In Q1 2025, get an AI tool that will help you improve productivity. AI skill is the universal blue collar skill of the 21st century worker and employers, looking for productivity, will expect you to be ready https://t.co/R0ha3hcI3N

Introduction to Philosophy God Knowledge and Consciousness | About Video

Fri, 11 Sep 2026 21:52:41 GMT

Learn how to ask and answer big questions. Pursue a verified certificate to have your work graded and commented upon by professional philosophers. Learn more and enroll at https://mitxonline.mit.edu/courses/course-v1:MITxT+24.00x/

What is Knowledge? Part 1: Steph Rennick for The Royal Institute of Philosophy

Fri, 11 Sep 2026 21:52:41 GMT

This is one of the Royal Institute of Philosophy’s 15-minute Philosophy Briefings, a series in which eminent philosophers provide their own view of a key philosophical topic, in straightforward and accessible language. Each one is designed to be a resource for anyone who wants to know more about these questions, whether you are covering them at A-level, teaching them at A-level, studying Philosophy at university, or are simply curious to know more. Dr Steph Rennick, Lecturer in Interactive Media at the University of Stirling, looks at what is involved in knowing something. This is Part One of her talk.

What is Knowledge? Part 2: Steph Rennick for The Royal Institute of Philosophy

Fri, 11 Sep 2026 21:52:41 GMT

Philosophy and Common Sense | Timothy Williamson | Methods of Philosophy: Lecture 1

Fri, 11 Sep 2026 21:52:41 GMT

Part of Prof. Timothy Williamson's ten-lecture series 'Methods of Philosophy', organised by Prof. Chen Bo of Peking University, which sponsored and hosted the lectures. ABSTRACT Common sense consists of the knowledge, beliefs, and ways of thinking generally accepted in a particular society, or by humanity as a whole, at a particular time. Some common sense beliefs are false but many are true, and there is much common sense knowledge. Every species of intelligent animal needs knowledge of its environment to survive; curiosity is a natural appetite for knowledge. Children ask questions, which are often deep or general enough to be called ‘scientific’ or ‘philosophical’. Although philosophy begins in common sense, it does not end there. It goes further, and sometimes corrects errors of common sense. Sometimes the reverse happens too: common sense corrects errors of philosophy. But how can common sense be a source of evidence without trapping us in dogmatic conservativism? Philosophy has been misled by the hopeless search for infallible sources of evidence (such as introspection). Instead, we should accept that all our sources of evidence are fallible, including common sense, and concentrate on being alert to recognize and correct our errors once they have come to light. RESPONDENTS Prof. Sebastian Sunday Grève (Peking University) Prof. Fei Duoyi (China University of Political Science and Law) SECTIONS 0:00 Lecture 1:32:20 Comments I 1:45:59 Comments II 1:48:20 Replies

PHILOSOPHY - Epistemology: Introduction to Theory of Knowledge [HD]

Fri, 11 Sep 2026 21:52:41 GMT

In this Wireless Philosophy video, Jennifer Nagel (University of Toronto) launches our Theory of Knowledge series. We look at the line between knowing and just believing something, focusing on factors like truth and confidence. Subscribe! http://bit.ly/1vz5fK9 More on Jennifer Nagel: http://bit.ly/1PLgDZZ ---- Wi-Phi @ YouTube: http://bit.ly/1PX0hLu Wi-Phi @ Khan Academy: http://bit.ly/1nQJcF7 Twitter: https://twitter.com/wirelessphi Facebook: http://on.fb.me/1XC2tx3 Instagram: @wiphiofficial ---- Help us caption & translate this video! http://amara.org/v/HpvU/

Introduction to Philosophy: Lecture 18 - Is Knowledge a State of Mind?

Fri, 11 Sep 2026 21:52:41 GMT

This course explores various ways of understanding the human self and its relation to the world. Through a consideration of what can be known, what is worth valuing, what reality is, and how human communities should be composed and regulated, the course deals with central themes that arise from the human quest for a deeper self-understanding. Learn more about Missouri State iCourses at http://outreach.missouristate.edu/icourses.htm

If you’re a grad student in electrical or communications engineering, treat this as a quiet power-up: METU OpenCourseWare on YouTube. Middle East Technical University put real lecture series online, no paywall, no fluff. The channel is a free archive of courses that sit right at the core of what you

Fri, 11 Sep 2026 21:45:21 GMT

If you’re a grad student in electrical or communications engineering, treat this as a quiet power-up: METU OpenCourseWare on YouTube. Middle East Technical University put real lecture series online, no paywall, no fluff. The channel is a free archive of courses that sit right at the core of what you actually use later: random signals, estimation, systems, and the math that wireless and comms rest on. The standout for communications people is EE 531 Probability and Stochastic Processes with Prof. Elif Uysal. Poisson processes, Markov chains, Gaussian processes, convergence, Brownian motion, this is the language of noise, fading channels, queues, and information theory. Pair it with EE 306 Signals and Systems II, which spends serious time on random variables, estimation, and stochastic modeling of signals rather than just LTI cookbook recipes. Those two playlists alone cover the theoretical backbone most master’s and PhD programs assume you already own. Circuit theory (EE 201/202) and electromagnetic theory extras round it out if you’re heading toward RF, microwave, or hardware-aware comms. What makes the teaching worth your time is the pace and honesty. Lectures are not 8-minute highlight reels. They work through measure-theoretic foundations when needed, then drop into examples (bus-waiting times at METU, filter design, MMSE estimators) so the abstractions stop floating. Grad students often discover they “knew” probability until they had to prove a convergence result or derive a Wiener filter from scratch. These recordings let you pause, rewind, and rebuild that muscle without waiting for office hours. The instructors treat the material as something you will use, not just pass. If you’re incoming or already in a program and feel shaky on stochastic processes or random signals, start here before your first midterm week. Watch EE 531 in order, keep a notebook of the definitions and the Gallager-style arguments, then jump to the signals playlist for the applied side. Use it as a second opinion next to your own lectures, or as the main resource if your department is light on theory. It will not replace a research group or a lab, but it will stop you from treating fading, noise, and detection as black boxes. Bookmark the channel, pick one playlist this week, and actually finish the first five lectures. That is the whole recommendation. Thorough instruction is rare and free here, use it. I have watched it and took notes once. I am not satisfied yet. I will do it again. Link: https://t.co/QRs4kU4G2T #OpenCourseWare #ElectricalEngineering #CommunicationsEngineering #SignalProcessing

A University of Washington professor put his entire graduate control theory course on YouTube for free

Fri, 11 Sep 2026 21:45:21 GMT

A University of Washington professor put his entire graduate control theory course on YouTube for free "Control Bootcamp", Steve Brunton, mechanical engineering, UW. Linear systems, stability and eigenvalues, controllability and observability, poly placement, the Kalman filter, LQR, LQG, robust control, model predictive control - the whole sequence, free, on his own channel. This is exact material every "learn robotics" roadmap means when it says "go understand control theory" - most PID and state-estimation tutorials are a five-minute summary of one lecture in here. Playlist: https://t.co/GzoXYYI4Hx P.S. this is the actual derivation every "Kalman filter in 5 minutes" blog post quietly skips.

Control Systems Engineering - Lecture 1 - Introduction

Fri, 11 Sep 2026 21:38:01 GMT

Lecture 1 for Control Systems Engineering (UFMEUY-20-3) and Industrial Control (UFMF6W-20-2) at UWE Bristol. Slides available here: http://www.cems.uwe.ac.uk/~bw-drew/CSE_Lecture_1-UWE.pdf. This lecture covers introduction to the module, control system basics with some examples, and modelling simple systems with differential equations.

Why Learn Control Theory

Fri, 11 Sep 2026 21:38:01 GMT

Get the map of control theory: https://www.redbubble.com/shop/ap/55089837 Download eBook on the fundamentals of control theory (in progress): https://engineeringmedia.com Welcome to my channel trailer and the first video for a course on control theory. In this video I present a few reasons why learning control theory is important and try to give some motivation to continue learning. Aircraft Flutter: https://www.youtube.com/watch?v=iTFZNrTYp3k Wine Glass vibrating: https://www.youtube.com/watch?v=McmtOxW7Tz0 HRG: http://en.wikipedia.org/wiki/Hemispherical_resonator_gyroscope Coriolis Effect: https://www.youtube.com/watch?v=rdGtcZSFRLk Primitive Equations: http://en.wikipedia.org/wiki/Primitive_equations Switching Power Regulators: http://cds.linear.com/docs/en/application-note/an25fa.pdf Errata: None that I know of! Don't forget to subscribe! I'm on Twitter @BrianBDouglas! If you have any questions on it leave them in the comment section below or on Twitter and I'll try my best to answer them. I will be loading a new video whenever I can and welcome suggestions for new topics. Please leave a comment or question below and I will do my best to address it. Thanks for watching!

A psychologist walked into a music academy in Berlin and accidentally gave the world one of the most comforting ideas ever sold.

Fri, 11 Sep 2026 21:30:41 GMT

A psychologist walked into a music academy in Berlin and accidentally gave the world one of the most comforting ideas ever sold. TALENT IS NOT REAL. You just need enough practice. His name was Anders Ericsson, and in 1993 he studied elite violinists at the Music Academy of West Berlin. The best players had started young, practiced for years, and by age 18 had accumulated thousands more hours of solo practice than the weaker groups. Ericsson argued that many traits people called “innate talent” were actually the result of intense deliberate practice over a decade or more. Then Malcolm Gladwell turned that research into a cultural law. 10,000 hours. The idea exploded because it was beautiful... like you don't need genetics, unfair starting line, or some uncomfortable hierarchy. You just time, effort, and the right kind of practice. But then the data got bigger. In 2014, Brooke Macnamara, David Hambrick, and Frederick Oswald pulled together 88 studies across music, sports, games, education, and professions. The result was brutal. Deliberate practice mattered. But it did not explain greatness. It explained 26% of performance differences in games. > 21% in music. > 18% in sports. > 4% in education. Less than 1% in professions. Across all domains, practice explained only about 12% of the variance. That means the rest did not disappear. It was sitting in the part nobody wanted to talk about. - Genes. - Memory. - Body type. - Processing speed. - Motivation. - Starting age. - Family environment. - Coaches. - Obsession. - Luck. And the most uncomfortable part is that even the urge to practice may not be fully separate from talent. A twin study of more than 10,000 Swedish twins found that music practice itself was substantially heritable, around 40% to 70%. The same genetic differences partly influenced both musical ability and the tendency to practice. Read that again. The hidden variable was not just practice. It was who becomes the kind of person who practices. That is the gene-environment feedback loop. A child shows early rhythm or pitch sensitivity. Adults notice. They get encouragement. Practice feels rewarding instead of humiliating. They continue. The gap widens. From the outside, it looks like discipline. Under the hood, it is biology and environment reinforcing each other. A newer twin study pushed this even harder. Researchers looked at musical expertise and lifetime practice hours in thousands of twins. On average, 50% of individual differences in musical expertise were due to genetic influences. More practice reduced environmental variance. But the genetic component did not go away. The relative genetic contribution actually increased with more practice. That does not mean practice is useless. It means practice is not an equalizer. Practice is a multiplier. And multipliers depend on what they are multiplying. This is why two people can train for the same number of hours, with the same coach, inside the same system, and one looks like they were born for it while the other looks like they are forcing their brain through concrete. One is not morally better. One may simply have better starting hardware for that domain. This is the part people get wrong. Genes are not destiny. But pretending they do not matter is not wisdom. It is cope guys. The honest answer is not “talent is fake.” The honest answer is: Talent is real. Practice is real. And greatness happens when both collide inside the right environment for long enough. The 10,000-hour rule gave people hope. The twin studies gave people humility. The truth is somewhere more useful than both. You cannot choose your starting hand. But you can choose the game where your hand compounds. Read the full study here for free: https://t.co/rJXyenie1L

AI Rare Disease Diagnoses: OpenAI o3 Solves 18 Cases Specialists Could Not | Jerry Owens, Techtimes

Fri, 11 Sep 2026 21:30:41 GMT

AI Rare Disease Diagnoses: OpenAI o3 Solves 18 Cases Specialists Could Not | Jerry Owens, Techtimes For nearly two decades, Kyra didn't know why her muscles were failing her. It started around age nine — slowing down in karate class, struggling at soccer, rising onto her toes when she walked. By 13, she was in a wheelchair and dependent on a ventilator. Specialists across multiple institutions examined her and found no answer. Then, shortly before her 28th birthday, a genetic counselor called with something unprecedented: a frameshift variant in a gene called HSPB8 had finally surfaced a name for her condition — myofibrillar myopathy. The diagnosis came not from a new test or a new doctor. It came from an AI model revisiting genomic data that human experts had already examined and set aside. A study published Thursday in NEJM AI — a collaboration between researchers at Boston Children's Hospital's Manton Center for Orphan Disease Research, Harvard University, and OpenAI — documents how OpenAI's o3 Deep Research reasoning model reanalyzed 376 previously unsolved cases of rare genetic disease in children. Following independent specialist review, additional laboratory testing, and CLIA-certified clinical confirmation, physicians established diagnoses in 18 of those cases — an additional diagnostic yield of 4.8% after earlier specialist analysis had come up empty. That figure carries more weight than it might appear to. These were not fresh presentations. Many had traveled through commercial and institutional genomic pipelines, been discussed by multidisciplinary teams, and been filed as unsolvable. For that population, 4.8% is not a rounding error — and for the 18 families now holding answers, the number is not a statistic at all. Why Stored Genomes Are Getting a Second Chance The structural problem in rare disease diagnosis is not simply that medicine lacks the right tests. For most patients, those tests have already been run. More than half of all patients with rare genetic conditions — an estimated 150 million people globally — never receive a confirmed diagnosis even after comprehensive genomic sequencing. A child's genome might have been fully sequenced in 2018 and found uninterpretable. The same genome, analyzed in 2026, may yield a diagnosis — because the knowledge used to interpret it has grown. Each year, hundreds of new gene-disease associations are identified and added to reference databases like ClinVar. A variant that was a dead end five years ago may now link to a newly described condition. The bottleneck has never been the sequenced data. It has been the human capacity to systematically re-examine it. A 2024 meta-analysis of 29 genomic reanalysis studies found an average additional diagnostic yield of 10% when previously unresolved cases were reviewed after a median of 24 months — yet widespread reanalysis remains rare in clinical practice because no institution has the specialist workforce to keep pace with the rate of new discoveries. Reanalysis is recommended in clinical genetics guidelines worldwide; it is almost never done at scale. Alan Beggs, director of the Manton Center, described the knowledge problem plainly: "Researchers like Catherine and me can't possibly keep 8,000 different diseases in our heads. That's the power of AI." His colleague Dr. Catherine Brownstein, the scientific director of the Manton Center's genetic investigations arm, framed the bottleneck in time rather than knowledge: "The bottleneck is time. An expert can devote only so much of their day to any one particular person." What the Boston Children's study establishes is that an AI reasoning model can serve as the scalability mechanism genomic reanalysis has never had. How OpenAI o3 Was Applied to Pediatric Genomics Diagnosis For each of the 376 unsolved cases, the research team built a de-identified data packet containing standardized clinical descriptions encoded in Human Phenotype Ontology (HPO) terms — a controlled vocabulary of 18,000-plus terms specifically designed for rare disease phenotyping — along with patient metadata such as age and sex, occasional clinician notes, and a filtered table of genetic variants annotated with ClinVar classifications, rarity scores, predicted protein effects, and inheritance patterns from available family members. This input structure matters for understanding what the model actually did. Unlike a standard large language model that reads text and generates a response in a single forward pass, OpenAI's o3 Deep Research uses reinforcement-learning-trained chain-of-thought reasoning, generating multiple candidate reasoning chains internally before selecting the most coherent one. The model was not asked to return a variant name. It was asked to show its reasoning: connecting clinical features, inheritance patterns, variant evidence, and relevant scientific literature into a coherent hypothesis that human reviewers could interrogate and, if warranted, test with additional experiments. Critically, the model did not diagnose anyone. Every output was reviewed by at least two independent researchers using the American College of Medical Genetics and Genomics and Association for Molecular Pathology (ACMG/AMP) framework — the clinical standard for classifying genetic variants into five tiers from pathogenic to benign. A diagnosis was recorded only after a qualified clinical team confirmed the finding in a CLIA-certified laboratory and returned the result to the family. The team validated the workflow before applying it to unsolved cases. Against cases with known diagnoses, the model correctly identified the causative gene and variant in 48 of 51 cases in an initial set, 45 of 57 in a neuromuscular cohort, and every correct gene in a 15-case long-read genome set. Crucially, the model's self-reported confidence scores tracked meaningfully with accuracy — cases where the model expressed high confidence tended to yield correct results more often, offering clinical reviewers a reliable triage signal about which outputs most warranted immediate laboratory follow-up. Results Across Four Patient Cohorts The 376 unsolved cases were drawn from four distinct clinical populations (below): The early psychosis cohort was small and carries a wide statistical margin; researchers note it should be interpreted with caution. The sudden unexpected death cohort's lower yield reflects how rarely pediatric cardiac events have a single-gene explanation identifiable through current sequencing approaches. Seven of the 18 new diagnoses were technically rediscoveries — answers already present in public databases but not previously connected to that patient's record — underscoring how fragmentation of medical information remains as significant a barrier as the absence of knowledge itself. What the Model Found That Human Reviewers Had Missed Several cases exposed reasoning capabilities that standard computational tools are not designed to produce. In one early-psychosis patient, the model detected a pattern of low-quality sequencing calls on chromosome 22 and connected them to the child's constellation of cardiac, immune, neurodevelopmental, and psychiatric symptoms — hypothesizing a 22q11.2 deletion consistent with DiGeorge syndrome. That structural chromosomal variant was later confirmed with targeted follow-up sequencing. It had not appeared in the original variant call table submitted to the model as input data; the model inferred the deletion from the signature of degraded signal quality. In other cases, the model proposed that two genes together better explained a complex clinical picture than any single gene could — identifying possible digenic causes in patients whose conditions had resisted standard single-gene explanations. This capability specifically addresses a known limitation of conventional genomic analysis pipelines, which are optimized to find monogenic causes and have difficulty surfacing compound genetic architectures without explicit prior hypotheses. The model also flagged one finding with potential significance beyond diagnosis: in a neurodevelopmental case, it identified an 11-amino-acid deletion in the gene S1PR1 in a patient with vitiligo and proposed a mechanistic hypothesis linking the deletion to altered pigment production and immune-cell signaling in the skin. The finding requires independent experimental validation but illustrates a potential function beyond diagnosis — using AI-assisted analysis to generate testable hypotheses about disease mechanisms in previously unexplained cases. Significant Caveats the Researchers Are Careful to Name The study is retrospective. The patient cohorts are heterogeneous in both disease category and prior sequencing approach. Reviewers were not blinded to the model's confidence scores, which could have introduced bias into which hypotheses received the most thorough follow-up. Time savings, false-positive burden, and effects on clinical care were not measured. Large language models can produce plausible-sounding explanations that fall apart on scrutiny — which is exactly why every model output in this study required independent human adjudication before being counted as a diagnosis. The study is not a roadmap for consumer-facing AI diagnostic tools. OpenAI stated directly that the research does not describe or endorse any intended consumer use of its models for diagnosing disease. The workflow described operates within a clinical infrastructure — specialists, certified laboratories, genetic counselors, and confirmation processes — that AI outputs cannot replace. What Comes Next for AI-Assisted Genomic Reanalysis The Manton Center will lead the next phase of this work through an OpenAI Foundation grant, with the goal of developing a platform-agnostic, low-cost genetics AI copilot that can help clinical teams analyze rare disease cases more efficiently — one not tied to any single commercial model. The study used OpenAI o3 Deep Res

🧬 Call for Doctoral Applications – Graduate School Life Science Munich (LSM), LMU Munich

Fri, 11 Sep 2026 21:30:41 GMT

🧬 Call for Doctoral Applications – Graduate School Life Science Munich (LSM), LMU Munich 🇩🇪 Munich, Germany (Planegg-Martinsried) Program: Graduate School Life Science Munich, Faculty of Biology Deadline: October 19, 2026 (annual call, open September 1 – October 19, 2026) Duration: 3–4 years, part-time (65%) 📋 About the Program LSM is LMU Munich's international doctoral programme spanning biochemistry, cell and developmental biology, ecology, genetics, microbiology, plant sciences, and zoology, based at the LMU Biocenter. This year offers two application pathways: 1️⃣ Pre-Defined Doctoral Projects (funding still being secured, with supervisor support to find fellowships/scholarships) • Antisense oligonucleotide treatment for MAST3-associated epilepsy (Prof. David Keays) • Avian cerebral organoids (Prof. David Keays) • Novel microtubule-associated proteins (Prof. David Keays) • Transcriptional networks in legume-rhizobia nitrogen-fixing symbiosis (Prof. Martin Parniske) • Natural genetic resources for insect-resistant fruit (Prof. Martin Parniske) 2️⃣ Independent Research Proposals (new this year) — candidates design their own proposal inspired by a participating faculty member's interests: • Prof. Claude Becker — epigenetics, epigenomics, plant-microbe interactions • Prof. Alexander Keller — biodiversity, AI/computer vision, plant-pollinator interactions • Prof. Dario Leister — photosynthesis regulation (CSC candidates only) • Prof. Michael Matschiner — phylogenetics, adaptive radiation, museum/fossil specimens • Dr. Natascha Turetzek — arthropod evo-devo, gene duplication, thermal stress 🔬 Ideal Candidate • MSc (or equivalent) in Biology, Biochemistry, Bioinformatics, Biophysics, Biotechnology, Medicine, or related field • Exceptionally, a 4-year Honours Bachelor's (240+ ECTS with thesis) accepted • Final degree examination completed within the last 4 years • Proficient written and spoken English ✨ Why Apply LSM offers genuine flexibility — either join an established project or shape your own research direction with faculty support — within a vibrant, international doctoral community at one of Europe's leading research universities. Benefits include structured mentoring via a Thesis Advisory Committee, transferable skills training, conference funding, and no tuition fees. 📍 Based at the LMU Biocenter in Planegg-Martinsried, just outside Munich, one of Germany's premier life sciences research hubs. 🔗 More Info: https://t.co/qqOpjN26DZ #PhDPosition #LifeSciences #LMUMunich #GraduateSchool #PlantGenetics #Neuroscience #Germany #Biocenter #AcademicJobs #Evolution

Understanding Genetics and What It Means (14 Minutes)

Fri, 11 Sep 2026 21:23:20 GMT

Understanding Genetics and What It Means explores the fundamental principles of genetics, explaining how genes influence traits, health, and diversity. In this video, we cover basic concepts like DNA, genes, inheritance, and genetic variation, along with their implications for medicine, agriculture, and personal identity. Discover how understanding genetics can help us make informed decisions about health, disease prevention, and scientific advancements. Whether you're a student, educator, or curious learner, this guide offers essential insights into what genetics truly means for our lives and society. Watch now to unlock the secrets of genetic science! 10 SEO-Optimized Hashtags #Genetics #HereditaryTraits #DNA #GeneticsEducation #GeneticResearch #GeneticInheritance #PersonalHealth #GeneticDiversity #Biology #GeneticScience 35 SEO Tags understanding genetics, what is genetics, basic genetics concepts, DNA and genes, inheritance explained, genetic variation, human genetics, genetics and health, genetic traits, heredity mechanisms, DNA structure, genetic inheritance, genetic diversity, genetics in medicine, genetic testing, gene function, genetics and evolution, genetic disorders, genetic engineering, genome analysis, genetic research, molecular genetics, genetics in agriculture, genetic inheritance patterns, DNA replication, gene expression, genetic mutation, genetic counseling, genetic mapping, biological inheritance, DNA sequencing, genetic studies, genetics education resources, genetic science breakthroughs

Genetics Explained in 6 Minutes

Fri, 11 Sep 2026 21:23:20 GMT

Dr BioTech Whisperer introduces the concept of Genetics. Learn about them in 6 minutes within this video. Thank you for your support. ☕ BUY ME A COFFEE Support us with a morning coffeebreak https://www.buymeacoffee.com/biotechW #genetics #student #genome #heredity #genomesequencing #gene #biotechnology #biotech

Introduction to Genetics (12 Minutes)

Fri, 11 Sep 2026 21:23:20 GMT

"Introduction to Genetics" is designed to provide viewers with a foundational understanding of genetics, the study of heredity and the variation of inherited characteristics. In this video, we will explore key concepts such as DNA structure, genes, chromosomes, and the principles of inheritance. Learn about the contributions of pioneering scientists like Gregor Mendel and how their discoveries laid the groundwork for modern genetics. We’ll discuss the role of genetics in health, disease, and biotechnology, as well as real-world examples and case studies that illustrate the impact of genetics on everyday life. Whether you’re a student, a healthcare professional, or simply interested in the science of heredity, this guide offers valuable insights to empower you on your journey. Watch now to discover the essentials of genetics! 10 SEO-Optimized Hashtags #Genetics #DNA #Heredity #Inheritance #Biology #GeneticScience #MendelianGenetics #Health #Biotechnology #LifeSciences 35 SEO Tags Introduction to Genetics, genetics, DNA, heredity, inheritance, biology, genetic science, Mendelian genetics, health, understanding key concepts in genetics, structure of DNA explained, role of genes and chromosomes, principles of inheritance, contributions of Gregor Mendel to genetics, empowering individuals through knowledge of genetics, fostering awareness of genetic principles, navigating the complexities of heredity, effective communication about genetic science, promoting appreciation for the impact of genetics on health, discussing challenges in genetic research, cultivating a culture of scientific inquiry, shaping effective strategies for teaching genetics, leading discussions on the future of genetic research, enhancing community engagement in science education, understanding the role of genetics in biotechnology, discussing the future of genetic applications, navigating economic considerations in genetic research, promoting accountability in genetic practices, empowering communities through knowledge of genetics, understanding the impact of genetics on disease prevention, effective feedback techniques in scientific discussions, exploring trends in genetics and its applications in health and science.

Genetics 101 | National Geographic

Fri, 11 Sep 2026 21:23:20 GMT

What is a genome, and how are traits passed from generation to generation? Learn how pea plants helped launch the study of genetics and how the field of genetics research has evolved over time. ➡ Subscribe: https://on.natgeo.com/4p5A0D6 About National Geographic: National Geographic is the world's premium destination for critically acclaimed storytelling around science and exploration. Discover amazing wildlife, ancient civilizations, hidden worlds, and the incredible wonders of our Earth. Through world-class scientists, photographers, journalists, and filmmakers, Nat Geo inspires fans of all ages to connect with, explore, and care about the world. Get More National Geographic: Official Site: https://nationalgeographic.com Instagram: https://instagram.com/natgeo Facebook: https://facebook.com/natgeo Threads: https://threads.com/@natgeo X: https://x.com/NatGeo LinkedIn: https://linkedin.com/company/national-geographic TikTok: https://tiktok.com/@natgeo Reddit: https://reddit.com/user/nationalgeographic Read more in "DNA, explained" https://on.natgeo.com/2V9t5ub Genetics 101 | National Geographic https://youtu.be/v8tJGlicgp8 National Geographic https://www.youtube.com/natgeo

The Science of Genetics Explained | Chapter 1 – Principles of Genetics (7th)

Fri, 11 Sep 2026 21:23:20 GMT

Last Minute Lecture is a student-run project and is currently funded entirely by students who believe educational resources should remain free and accessible. If Last Minute Lecture has helped you study, please consider supporting the project. ❤️ https://lastminutelecture.com/support/ All chapters are now available for free on our new platform: https://lastminutelecture.com 📚 Genetics is the essential biological science dedicated to studying DNA, the intricate macromolecule housed within the genome—the complete informational blueprint used by cells to maintain the living state. This foundational science, though relatively young, has been built upon three monumental milestones: Gregor Mendel’s pioneering work identifying genes as discrete hereditary factors and establishing the fundamental rules of inheritance through alleles, James Watson and Francis Crick's elucidation of the double helix structure of deoxyribonucleic acid (DNA) in 1953, and the expansive Human Genome Project, which provided the comprehensive sequence of human DNA and launched the field of genomics. To function as the hereditary material, DNA must satisfy three crucial criteria: the ability to replicate precisely via a complementary mechanism where strands act as templates; the ability to encode instructions through sequences of nucleotides; and the capacity to change through mutation. Genetic information is expressed following the central dogma, where a gene’s DNA sequence undergoes transcription to produce a complementary RNA transcript, which then serves as messenger RNA (mRNA) to be read in three-nucleotide units called codons during translation, ultimately directing the synthesis of specific amino acid sequences in polypeptides, the building blocks of the proteome. Mistakes during replication or damage can cause mutations, such as the single base-pair change responsible for sickle-cell disease, but this resulting genetic variability is simultaneously the engine for biological evolution, allowing researchers to study historical relationships and construct phylogenetic trees using DNA sequence data. Genetic analysis is conducted across three levels: classical or transmission genetics, which focuses on tracking trait inheritance across generations; molecular genetics, which involves isolating, sequencing, and manipulating DNA; and population genetics, which examines allele frequencies and genetic variability within groups. The science is deeply integrated into human endeavors, transforming agriculture through selective breeding and the creation of genetically modified organisms (GMOs) like BT corn, revolutionizing medicine through advanced diagnostic testing, the industrial-scale production of therapeutic human proteins such as insulin, and emerging techniques like human gene therapy, while also generating crucial legal, economic, and philosophical inquiries regarding human nature and societal organization. Thank you for being a part of our little Last Minute Lecture family! ⚠️ Disclaimer: These summaries are created for educational and entertainment purposes only. They provide transformative commentary and paraphrased overviews to help students understand key ideas from the referenced textbooks. Last Minute Lecture is not affiliated with, sponsored by, or endorsed by any textbook publisher or author. All textbook titles, names, and cover images—when shown—are used under nominative fair use solely for identification of the work being discussed. Some portions of the writing and narration are generated with AI-assisted tools to enhance accessibility and consistency. While every effort has been made to ensure accuracy, these materials are intended to supplement—not replace—official course readings, lectures, or professional study resources. Always refer to the original textbook and instructor guidance for complete and authoritative information.

Introduction To Genetics | Genetics Ep. 1

Fri, 11 Sep 2026 21:23:20 GMT

Welcome to the my YT course for genetics! In this video I go over a broad overview of genetics and some key terminology. If you have any questions, let me know in the comments. Thanks and hope you enjoy! Content is prepared live on Twitch so follow me there for more discussions! https://twitch.tv/drwd40 🧬 Interested in more genetics? Check out the playlist here: https://youtube.com/playlist?list=PLddaXzcsfB5VwKLFwoNogREmcCxKqKdZS I also have some other course playlists! 🦴 Anatomy and Physiology 1: https://youtube.com/playlist?list=PLddaXzcsfB5XQvsxVzvYB3XhuxIExDd_8 🧠 Anatomy and Physiology 2: https://youtube.com/playlist?list=PLddaXzcsfB5VUhJ-8QpTQEKxJXK0o6iwB ➡ Check out my social pages too! https://streamerlinks.com/DrWD40

How to get from high school math to cutting-edge ML/AI: a detailed 4-stage roadmap with links to the best learning resources that I'm aware of.

Fri, 11 Sep 2026 21:16:01 GMT

How to get from high school math to cutting-edge ML/AI: a detailed 4-stage roadmap with links to the best learning resources that I'm aware of. I recently talked to a number of people who work in software and want to get to the point where they can read serious ML/AI papers like Denoising Diffusion Probabilistic Models (https://t.co/T9UJi4zQuQ). But even though they did well in high school math, even AP Calculus, maybe even learned some undergraduate math... the math in these cutting-edge ML papers still looks like hieroglyphics. So, how do you get from high school math to cutting-edge ML? Here’s a 4-stage roadmap. I'll start by briefly describing all the stages – and then I’ll go back to each stage for a deep dive where I fully explain the rationale and point you to resources that you can use to guide your learning. Stage 1: Foundational Math. All the high school and university-level math that underpins machine learning. All of algebra, a lot of single-variable calculus / linear algebra / probability / statistics, and a bit of multivariable calculus. Stage 2: Classical Machine Learning. Coding up streamlined versions of basic regression and classification models, all the way from linear regression to small multi-layer neural networks. Stage 3: Deep Learning. Multi-layer neural networks with many parameters, where the architecture of the network is tailored to the specific kind of task you’re trying to get the model to perform. Stage 4: Cutting-Edge Machine Learning. Transformers, LLMs, diffusion models, and all the crazy stuff that’s coming out now, that captured your interest to begin with. Note that I've spent the past 5+ years working on resources to support learners in stages 1-2, and there is a general lack of serious yet friendly resources in those stages, so I'm going to be including my own resources there (along with some others). But in stages 3-4, all the resources I'll reference are things I'm not affiliated with in any way. Alright, let's dive in! === Stage 1: Foundational Math === There’s a lot of math underpinning ML: all of high school algebra, a lot of single-variable calculus / linear algebra / probability / statistics, and a bit of multivariable calculus. Last year, I spent some time mapping all this math out at a topic-by-topic level. In many of these math courses, some topics are absolutely essential to know for ML, while other topics are unnecessary. For instance, in multivariable calculus: • You absolutely must know how to compute gradients (they show up all the time in the context of gradient descent when training ML models), and you need to be solid on the multivariable chain rule, which underpins the backpropagation algorithm for training neural networks. • But on the other hand, many other multivariable calculus topics like divergence, curl, spherical coordinates, and Stokes' theorem don’t really show up anywhere in ML. Once I mapped out the list of required topics, we put together a Mathematics for Machine Learning course whose table of contents can be viewed here: https://t.co/0ES0kBkb90 (To see the list of all 242 individual topics, click on “Content” and then click to expand the Unit boxes.) If you’re working on @_MathAcademy_'s maximum-efficiency learning system, you can learn all this content in about 70 hours if you know your math up through single-variable calculus. (And if you're missing background knowledge, that's totally fine – the diagnostic assessment will automatically figure out the prerequisite material that you need to learn in algebra, calculus, etc. and add it to your learning plan. Even if you've completely forgotten all the math you learned and need to rebuild your foundations from the ground up, starting with fractions, we've got you covered: https://t.co/lL0CB10ARz) It’s also possible to learn these topics through free online resources. For instance, • Khan Academy covers arithmetic up through high school math, • OpenStax covers high school and a bit of early university math, • MIT OpenCourseWare covers plenty of university math, and • for any given math topic, there are plenty of notes online and usually plenty of videos on YouTube. However, to make it all the way through this Stage 1 using free resources, you'll have to piece together a hodgepodge of scattered educational content, and there will be a lot more unproductive friction that will not only slow you down but also make you more likely to get overwhelmed and give up. (Personally, I self-studied a bunch of math subjects through MIT OpenCourseWare and a variety of textbooks when I was in high school. These were good resources and I came a long way with them, but for the amount of effort that I put into learning, I could have gone a lot further if my time were used more efficiently. More info: https://t.co/FbUw0ece86) For most people, this stage of foundational math learning is a make-or-break moment and the hardest part of their machine learning journey. Learning the foundational math for machine learning is like learning how to read: achieving literacy opens up a world of further learning experiences, but without literacy, that world is completely closed off to you. === Stage 2: Classical Machine Learning === Once you have your math foundations in place, you’re ready to start coding up streamlined versions of basic ML models from linear regression to small multi-layer neural networks. But don’t try to jump into the fancy cutting-edge ML models just yet – if you do, you’re going to be confused by a lot of patterns that come from classical machine learning. Let me give a concrete example of what I mean by this. Let’s go back to that paper Denoising Diffusion Probabilistic Models (https://t.co/T9UJi4zQuQ). If you look at the bottom of page 3, you’ll see that equation (8) looks something like the following, but with a lot more subscripts and function arguments (which I’ve left out for simplicity): L = E[ 1/2σ || μ~ – μ ||^2 ] + C Even if you know • what the E means (“expectation” from probability/statistics), • what the σ means (“standard deviation” also from probability/statistics), • what the || means ("vector norm" from linear algebra and multivariable calculus), • what the "C" means (arbitrary "constant of integration" that vanishes when we take the derivative, covered in calculus), • and so on... the way these symbols are arranged here might look really confusing. But if you know your classical machine learning, the equation immediately hits you as resembling a “loss function” that measures the average squared difference between two quantities. (It's no coincidence that the authors used the letter "L" on the left-hand side of the equation – "L" for "loss.") In classical ML, in that average squared difference between two quantities, one of those quantities comes from your model, and the other comes from a data set, so the loss function in a sense measures how “wrong” your model is in its predictions across the data set. (Loosely speaking, the goal of “training” a ML model is to minimize the loss function, that is, to adjust the parameters of the model to minimize how “wrong” the model is about the data.) My point here is that if you know about these modeling patterns from classical ML, then you’ll immediately have intuition about the equations you see in cutting-edge ML papers. It’s like how, if a musician has practiced playing their scales and identifying musical keys, they can often jump into any song – even one they’ve never heard it before – and improvise in a way that sounds good, because their understanding of underlying musical patterns has given them a level of “feeling” or “intuition” about the new musical structure in which they’re operating. Similarly, an experienced software developer who knows all sorts of design patterns might be able to get a sense of how a piece of code operates just by noticing patterns in the high-level structure – even though they haven’t actually gone through line by line to understand the precise operations that are happening in the code. If you know your classical machine learning, the same thing will happen to you when reading modern ML papers. Just glancing at equations and diagrams, you'll find meaningful features jumping out at you in intuitive ways. Reading the paper will feel like looking at a "picture" where you get a high-level sense of what's going on and then zoom in on the details afterwards. But if you don't know your classical ML, then good luck reading modern ML papers, because the authors are not going to spell all this out for you! So, once you have your math foundations in place, how can you get up to speed classical machine learning? One problem with many ML courses is that they don’t have students implement the key models from scratch. Now, I’m not saying you have to implement a full-fledged ML library with all the bells and whistles of scikit-learn, pytorch, tensorflow, keras, etc., … but simply coding up a streamlined version of each basic ML model along the way from linear regression to small multi-layer neural networks will do wonders for your understanding. This was the premise of a series of quantitative coding classes that I developed and taught from 2020-23. • First, students implemented basic ML algorithms from scratch, coding up streamlined versions of polynomial and logistic regression, k-nearest neighbors, k-means clustering, and parameter fitting via gradient descent. • Then, they implemented more advanced ML algorithms such as decision trees and neural networks (still streamlined versions), and they reproduced academic research papers in artificial intelligence leading up to Blondie24, an AI computer program that taught itself to play checkers. I wrote a textbook for those courses, Introduction to Algorithms and Machine Learning, which includes all the instructional material and exercises that I used during the courses. It’s freely available here: https://t.co/

How to get from high school math to cutting-edge ML/AI: a detailed 4-stage roadmap with links to the best learning resources that I'm aware of.

Fri, 11 Sep 2026 21:16:01 GMT

How to get from high school math to cutting-edge ML/AI: a detailed 4-stage roadmap with links to the best learning resources that I'm aware of.

Fri, 11 Sep 2026 21:16:01 GMT

How to get from high school math to cutting-edge ML/AI: a detailed 4-stage roadmap with links to the best learning resources that I'm aware of.

Fri, 11 Sep 2026 21:16:01 GMT

How to get from high school math to cutting-edge ML/AI: a detailed 4-stage roadmap with links to the best learning resources that I'm aware of.

Fri, 11 Sep 2026 21:16:01 GMT

Tim Roughgarden's lecture note's compilation:

Fri, 11 Sep 2026 21:16:01 GMT

Tim Roughgarden's lecture note's compilation: Available at: https://t.co/ElOiMvRY9s Includes notes about: * Foundations of Blockchain Protocols * Modern Algorithmic Toolbox * A Second Course in Algorithms * Beyond Worst-Case Analysis * Incentives in Computer Science * Algorithmic Game Theory * Frontiers in Mechanism Design * Communication Complexity (for Algorithm Designers) * Miscellaneous Lecture Notes

Berkeley teaches the courses that turn people into real engineers, and every single one is online for free.

Fri, 11 Sep 2026 21:16:01 GMT

Berkeley teaches the courses that turn people into real engineers, and every single one is online for free. Bootcamps teach you to copy a React tutorial. These teach you how a computer actually works. Here are 10 to bookmark. 1. CS 61A: Structure and Interpretation of Programs https://t.co/2BN5na5sLt The course that breaks your brain and rebuilds it. It doesn't teach you a language. It teaches you how to think in code, how to build small pieces and stack them into something huge. This is the class self-taught devs skip and then wonder why they hit a wall. Every lecture, lab, and solution is free. 2. CS 61B: Data Structures The one that separates people who can code from people who get hired. You build the things underneath everything, the lists, the trees, the maps, from scratch, in Java, until you understand why your code is slow and how to fix it. The exact gap most bootcamp grads have. 3. CS 61C: Machine Structures Where you finally learn what's happening under the code. Memory, the processor, how a single line you write turns into electricity moving through a chip. Most developers have no idea. The ones who do debug things nobody else can. 4. CS 70: Discrete Math and Probability The math that actually matters for computer science, taught the way engineers use it, not the way a textbook buries it. Logic, proofs, probability. The quiet foundation under algorithms, cryptography, and every machine learning model you'll ever touch. 5. CS 188: Introduction to Artificial Intelligence Berkeley's full AI course. Search, planning, decision-making under uncertainty, and the ideas that everything from self-driving cars to game engines are built on. The class that teaches you how machines make choices, free and complete online. 6. CS 189: Introduction to Machine Learning The heavier one. The actual math and mechanics behind how models learn, taught at the level Berkeley expects from people who go build this stuff at real labs. Not a "prompt engineering" course. The real thing. 7. CS 186: Introduction to Database Systems How databases actually work under the hood, how they store, find, and protect data at massive scale. The skill every backend job quietly depends on and almost no bootcamp covers past "here's how to write a query." 8. Data 8: Foundations of Data Science The gentlest starting point on this list. It teaches you to program in Python and reason with data at the same time, built for people with zero coding experience. The perfect on-ramp before CS 61A if raw code feels like too much too fast. 9. CS 161: Computer Security How systems get broken, and how you stop it. You learn to think like an attacker so you can build things that hold up. One of the highest-paid skills in tech, taught by Berkeley, sitting online for free. 10. CS 162: Operating Systems The deep end. How the software that runs every other piece of software actually works, threads, memory, files, the whole machine. Finish this and very little about a computer stays mysterious. Bootcamps sell you a shortcut and a certificate. Berkeley left the real thing unlocked and told nobody to hurry. The people who become dangerous engineers aren't smarter. They just went through the front door everyone else walked past.

Algorithms and Data Structures Tutorial - Full Course for Beginners

Fri, 11 Sep 2026 21:08:42 GMT

In this course you will learn about algorithms and data structures, two of the fundamental topics in computer science. There are three main parts to this course: algorithms, data structures, and a deep dive into sorting and searching algorithms. By the end, you will understand what algorithms and data structures are, how they are measured and evaluated, and how they are used to solve problems. This course was developed by Pasan Premaratne and Jay McGavren. It was made possible by a grant from teamtreehouse.com ❤️ Try interactive Algorithms courses we love, right in your browser: https://scrimba.com/freeCodeCamp-Algorithms (Made possible by a grant from our friends at Scrimba) ⭐️ Course Contents ⭐️ ⌨️ (0:00:00) Introduction to Algorithms ⌨️ (1:57:44) Introduction to Data Structures ⌨️ (4:11:02) Algorithms: Sorting and Searching ⭐️ Code Snippets for Course ⭐️ 💻 Introduction to Algorithms: ⌨️ Algorithms in Code: 🔗 Linear Search Implementations: https://teamtreehouse.com/library/introduction-to-algorithms/algorithms-in-code/linear-search-implementations 🔗 Binary Search Implementations: https://teamtreehouse.com/library/introduction-to-algorithms/algorithms-in-code/binary-search-implementations 💻 Introduction to Data Structures ⌨️ Exploring Arrays: 🔗 Array Characteristics and Storage: https://teamtreehouse.com/library/introduction-to-data-structures/exploring-arrays/array-characteristics-and-storage 🔗 Operations on Arrays: https://teamtreehouse.com/library/introduction-to-data-structures/exploring-arrays/operations-on-arrays ⌨️ Building a Linked List: 🔗 Singly and Doubly Linked Lists: https://teamtreehouse.com/library/introduction-to-data-structures/building-a-linked-list/singly-and-doubly-linked-lists-2 🔗 Linked List Operations: https://teamtreehouse.com/library/introduction-to-data-structures/building-a-linked-list/linked-lists-operations ⌨️ The Merge Sort Algorithm: 🔗 Merge Sort Implementations: https://teamtreehouse.com/library/introduction-to-data-structures/the-merge-sort-algorithm/merge-sort-implementations 🔗 Alternate Versions of Merge Sort: https://teamtreehouse.com/library/introduction-to-data-structures/the-merge-sort-algorithm/alternate-versions-of-merge-sort ⌨️ Merge Sort and Linked Lists: 🔗 Implementing Merge Sort on Linked Lists: https://teamtreehouse.com/library/introduction-to-data-structures/merge-sort-and-linked-lists/implementing-merge-sort-on-linked-lists 💻 Algorithms: Sorting and Searching ⌨️ Sorting Algorithms: 🔗 Code for Bogosort: https://teamtreehouse.com/library/algorithms-sorting-and-searching/sorting-algorithms/code-for-bogosort 🔗 Code for Selection Sort: https://teamtreehouse.com/library/algorithms-sorting-and-searching/sorting-algorithms/code-for-selection-sort 🔗 Code for Quicksort: https://teamtreehouse.com/library/algorithms-sorting-and-searching/sorting-algorithms/code-for-quicksort 🔗 Code for Merge Sort: https://teamtreehouse.com/library/algorithms-sorting-and-searching/sorting-algorithms/code-for-merge-sort ⌨️ Searching Names: 🔗 Code for Linear Search: https://teamtreehouse.com/library/algorithms-sorting-and-searching/searching-names/code-for-linear-search 🔗 Code for Binary Search: https://teamtreehouse.com/library/algorithms-sorting-and-searching/searching-names/code-for-binary-search -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news

CS50x - Lecture 3 - Algorithms

Fri, 11 Sep 2026 21:08:42 GMT

*** This is CS50, Harvard University's introduction to the intellectual enterprises of computer science and the art of programming. *** TABLE OF CONTENTS 00:00:00 - Introduction 00:00:43 – Overview 00:11:55 – Searching 00:14:27 – Linear Search 00:17:40 – Binary Search 00:27:01 – Running Time 00:38:54 – search.c 00:50:12 – phonebook.c 00:56:09 – Structs 01:02:13 – Sorting 01:12:20 – Selection Sort 01:20:12 – Bubble Sort 01:29:11 – Recursion 01:36:02 – iteration.c 01:39:44 – recursion.c 01:45:46 – Merge Sort 01:57:23 – Sort Race *** HOW TO SUBSCRIBE http://www.youtube.com/subscription_center?add_user=cs50tv HOW TO TAKE CS50 edX: https://cs50.edx.org/ Harvard Extension School: https://cs50.harvard.edu/extension Harvard Summer School: https://cs50.harvard.edu/summer OpenCourseWare: https://cs50.harvard.edu/x HOW TO JOIN CS50 COMMUNITIES Bluesky: https://bsky.app/profile/cs50.harvard.edu Discord: https://discord.gg/cs50 Ed: https://cs50.edx.org/ed Facebook Group: https://www.facebook.com/groups/cs50/ Faceboook Page: https://www.facebook.com/cs50/ GitHub: https://github.com/cs50 Gitter: https://gitter.im/cs50/x Instagram: https://instagram.com/cs50 LinkedIn Group: https://www.linkedin.com/groups/7437240/ LinkedIn Page: https://www.linkedin.com/school/cs50/ Medium: https://cs50.medium.com/ Quora: https://www.quora.com/topic/CS50 Reddit: https://www.reddit.com/r/cs50/ Slack: https://cs50.edx.org/slack Snapchat: https://www.snapchat.com/add/cs50 SoundCloud: https://soundcloud.com/cs50 Stack Exchange: https://cs50.stackexchange.com/ Telegram: https://t.me/cs50x Threads: https://www.threads.net/@cs50 TikTok: https://www.tiktok.com/@cs50 Twitter: https://twitter.com/cs50 Twitter Community: https://twitter.com/i/communities/1722308663522594923 YouTube: http://www.youtube.com/cs50 HOW TO FOLLOW DAVID J. MALAN Facebook: https://www.facebook.com/dmalan GitHub: https://github.com/dmalan Instagram: https://www.instagram.com/davidjmalan/ LinkedIn: https://www.linkedin.com/in/malan/ Quora: https://www.quora.com/profile/David-J-Malan Threads: https://www.threads.net/@davidjmalan TikTok: https://www.tiktok.com/@davidjmalan Twitter: https://twitter.com/davidjmalan *** CS50 SHOP https://cs50.harvardshop.com/ *** LICENSE CC BY-NC-SA 4.0 Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License https://creativecommons.org/licenses/by-nc-sa/4.0/ David J. Malan https://cs.harvard.edu/malan malan@harvard.edu

Data Structures and Algorithms Full Course 📈

Fri, 11 Sep 2026 21:08:42 GMT

Data Structures and Algorithms full course tutorial java #data #structures #algorithms ⭐️Time Stamps⭐️ #1 (00:00:00) What are data structures and algorithms? 📈 #2 (00:02:20) Stacks 📚 #3 (00:11:45) Queues 🎟️ #4 (00:21:51) Priority Queues 🥇 #5 (00:26:51) Linked Lists 🔗 #6 (00:40:14) Dynamic Arrays 🌱 #7 (01:04:37) LinkedLists vs ArrayLists 🤼‍♂️ #8 (01:13:07) Big O notation 📈 #9 (01:19:32) Linear search ⬇️ #10 (01:23:13) Binary search 🪓 #11 (01:32:44) Interpolation search ❓ #12 (01:41:05) Bubble sort 🤿 #13 (01:48:14) Selection sort 🔦 #14 (01:56:35) Insertion sort 🧩 #15 (02:03:40) Recursion 😵 #16 (02:11:58) Merge sort 🔪 #17 (02:25:07) Quick sort ⚡ #18 (02:38:57) Hash Tables #️⃣ #19 (02:52:21) Graphs intro 🌐 #20 (02:57:39) Adjacency matrix ⬜ #21 (03:07:30) Adjacency list 📑 #22 (03:15:59) Depth First Search ⬇️ #23 (03:23:40) Breadth First Search ↔️ #24 (03:30:20) Tree data structure intro 🌳 #25 (03:33:14) Binary search tree 🔍 #26 (03:53:38) Tree traversal 🧗 #27 (03:57:35) Calculate execution time ⏱️ Code from each video can be found pinned in the comments section of the original series playlist music credits 🎼 : =========================================================== Up In My Jam (All Of A Sudden) by - Kubbi https://soundcloud.com/kubbi Creative Commons — Attribution-ShareAlike 3.0 Unported— CC BY-SA 3.0 Free Download / Stream: http://bit.ly/2JnDfCE Music promoted by Audio Library https://youtu.be/tDexBj46oNI =========================================================== Twelve Speed (Alone Time Vol. 2) by - Slynk Link - https://youtu.be/jsTEUi-kegE =========================================================== YouTube Audio Library Vol. 3 by - Bad Snacks Link - https://youtu.be/WyOdBcADtp8 =========================================================== Copyright Disclaimer: This video is the intellectual property of Bro Code. All rights reserved. No part of this video may be reproduced, distributed, or transmitted in any form or by any means, including but not limited to recording, uploading, or other electronic or mechanical methods, without my written permission, except in the case of brief quotations embodied in critical reviews and certain other noncommercial uses permitted by copyright law.

Annual John Bell Day Lecture 2025 | Queen's University Belfast

Fri, 11 Sep 2026 21:01:23 GMT

This event is hosted by the Chief Executives’ Club at Queen’s in association with the School of Mathematics and Physics. The lecture titled “Loophole-free Bell Inequality Violation with Superconducting Circuits” was delivered by Professor Andreas Wallraff. Andreas Wallraff is Full Professor for Solid-State Physics in the Department of Physics at ETH Zurich. His work focuses on the experimental investigation of quantum effects in superconducting electronic circuits for fundamental quantum optics experiments and for applications in quantum information processing. His group at ETH Zurich researches micro- and nano-electronics as well as hybrid quantum systems combining superconducting electronic circuits with semiconductor quantum dots, making use of fast and sensitive microwave techniques at ultra-low temperatures. Subscribe NOW to Queen’s University Belfast: http://bit.ly/1Y24vux MORE from Queen’s University Belfast: Instagram: https://www.instagram.com/QUBelfast Facebook: https://www.facebook.com/QUBelfast Twitter: https://twitter.com/QUBelfast TikTok: https://www.tiktok.com/@qubelfast Study at Queen’s: View all courses: https://www.qub.ac.uk/courses/ Virtual Tour: https://virtualexperience.qub.ac.uk/welcome Queen’s University Belfast is a Russell Group university based in Belfast, Northern Ireland. Founded in 1845, we are the 9th oldest university in the UK and provide a world-class, research-led education to thousands of students from around the world each year. Based in the heart of Belfast, a vibrant UK capital city that is safe and easy to get around, and with excellent employment opportunities and support for your future career, Queen’s really is a place like no other. https://www.qub.ac.uk/Study/Why-Study-at-Queens/ http://qub.ac.uk/

Global Thinkers: On the Equality of All Things | Carlo Rovelli

Fri, 11 Sep 2026 21:01:23 GMT

On May 14, 2026, the Berggruen Global Thinkers Series presented the lecture “On the Equality of All Things” held at Peking University’s Centennial Memorial Hall. The lecture was delivered by the renowned theoretical physicist Carlo Rovelli, who drew from his upcoming book under the same name (On the Equality of All Things, 齊物論) following the famed Zhuangzi chapter. The Berggruen Center’s Academic Advisory Council Co-Chair Roger Ames hosted the event. Rovelli contends that contemporary physics—particularly quantum mechanics and general relativity—compels us to undertake a profound revision of our understanding of reality, one with far-reaching philosophical implications. These theories encourage a view of the world as constituted by processes and relations, rather than by entities possessing independent existence; they challenge metaphysical dichotomies such as subject/object, matter/spirit, and living/non-living; and they invite us to abandon the notion of any ultimate or privileged foundation. In this respect, Eastern classical thinkers such as Nagarjuna and Zhuangzi, together with strands of Western philosophy, offer conceptual frameworks that resonate with and help illuminate these recent developments in our understanding of the world.

We are honored to announce that Dr. Kevin H. Knuth, esteemed physicist and member of the Society’s Board of Advisors, will deliver the 2025 J. Allen Hynek Distinguished Lecture. This is our flagship annual event dedicated to advancing the scholarly study of UAP in the spirit of Dr. Hynek’s legacy.

Fri, 11 Sep 2026 21:01:23 GMT

We are honored to announce that Dr. Kevin H. Knuth, esteemed physicist and member of the Society’s Board of Advisors, will deliver the 2025 J. Allen Hynek Distinguished Lecture. This is our flagship annual event dedicated to advancing the scholarly study of UAP in the spirit of Dr. Hynek’s legacy. https://t.co/ADaZUU8W0c Dr. Knuth is a Professor of Physics at the University at Albany (SUNY), Editor-in-Chief of Entropy, and a former NASA - National Aeronautics and Space Administration research scientist. His groundbreaking work spans the foundations of physics, quantum information theory, autonomous robotics, the search for exoplanets, and the scientific investigation of UAP. With over 100 peer-reviewed publications and appearances in major documentaries on Netflix and National Geographic, Dr. Knuth is helping lead a new era in anomaly science. In this year’s Hynek Lecture, Dr. Knuth will chart the rise of the scientific movement to monitor and analyze UAP. He will dispel common misconceptions and provide a comprehensive overview of global efforts to study these phenomena using sensor data, physical evidence, historical records, and behavioral patterns. His lecture will present a compelling case that UAP are not only real and recurring, but may also exhibit performance characteristics that challenge our current understanding of physics and technology. This event continues our commitment to intellectual rigor, transparency, and academic courage in the pursuit of understanding anomalous phenomena. Date: Saturday, August 23rd Time: 2:00 PM – 5:00 PM EST Location: Online via Google Meets

New discoveries in quantum science raise profound questions, but how does this emerging branch of research relate to the Catholic faith? That question is at the heart of a new international gathering of physicists, philosophers, and theologians taking place July 12–15 at Chapman University in Orange

Fri, 11 Sep 2026 21:01:23 GMT

New discoveries in quantum science raise profound questions, but how does this emerging branch of research relate to the Catholic faith? That question is at the heart of a new international gathering of physicists, philosophers, and theologians taking place July 12–15 at Chapman University in Orange, California. The university will host the inaugural meeting of the “Interface Between Quantum Science and Technology, Philosophy, and Catholic Theology” where topics will include quantum entanglement, quantum indeterminacy, hylomorphism, and electromagnetic radiation — with Catholic theology integrated into most lectures. Daily Mass will also be celebrated. All talks will be recorded and made available afterward. Organizers include Professor Vincenzo Tamma, founding director of the Quantum Science and Technology Hub at the University of Portsmouth in the U.K., and Jesuit Father Robert Spitzer, director of the Magis Center, host of EWTN’s “Father Spitzerʼs Universe,” and a prolific writer on faith and science. Local organizers include Chapman professors Andrew Jordan and Daniele Struppa, both from Chapman’s Institute for Quantum Studies. Physics is an area of science remarkably appealing to Catholics, revealing as it does the created world’s order and intelligibility. A number of famous physicists have been deeply religious Catholics, including Georges Lemaître, a Belgian priest and astrophysicist who first proposed the big bang theory; Victor Hess, who won the Nobel Prize for discovering cosmic rays; and modern scientists like Cornell’s Jonathan Lunine, Vanderbilt’s Robert Scherrer, and the University of Delaware’s Stephen Barr, who will deliver a keynote at Chapman. To that end, a public keynote titled “Is the Notion of God Meaningful to Scientific Culture? The Openness of Science to the Quest for Truth and Meaning” will be delivered by Father Giuseppe Tanzella-Nitti of Rome’s Pontifical University of the Holy Cross who is also an adjunct scholar at the Vatican Observatory. The lecture is free but registration is required. Beyond that public lecture, the conference itself is invitation-only. More at: https://t.co/HLEC10QxLx

January 29, 1926: the great theoretical physicist Abdus Salam was born in the Punjab Province of British India (now in Pakistan).

Fri, 11 Sep 2026 21:01:23 GMT

January 29, 1926: the great theoretical physicist Abdus Salam was born in the Punjab Province of British India (now in Pakistan). In 1979 he shared the Nobel Prize for Physics with Steven Weinberg and Sheldon Glashow, "for their contribution to the theory of the unified weak and electromagnetic interaction between elementary particles, including, inter alia, the prediction of the weak neutral current ". This pioneering work has left an indelible mark on scientific knowledge, on the path opened by Isaac Newton and James Clerk Maxwell who formulated the great unification theories of classical physics. In his Nobel Lecture in December 1979, Abdus Salam said: “Scientific thought is the common heritage of mankind ”. In fact, he will always be remembered also for his invaluable contribution to the diffusion of science in developing countries and for the foundation (1964) of the International Centre for Theoretical Physics (ICTP) in Trieste, Italy. To ICTP he gave a unique imprint in the field of research and the dissemination of knowledge so much so that the Centre has become a model for many other institutions in various places of the world. Abdus Salam helped create other research centres and several international foundations. He was a person of great humanity – with his dreams often ahead of his time. As a great scientist of the 20th century, he gave rise to a fundamental development in science that significantly changed the view of the world. However, in addition to the cognitive field, his impact was also notable on the scientific community. His dream was to create a World University that connected international centres and scientific institutions into one large network. In Physics, Abdus Salam was interested in every possible development: his curiosity extended to many fields. The constant flow of his original ideas led to the unified theory of weak and electromagnetic interactions in the domain of the quantum world of elementary particles. Towards the end of his life, he became interested in formulating an even more general theory capable of unifying all the fundamental interactions of Nature, including strong interactions and gravity. His image of the world was that of a Nature described by simple and elegant physical laws. So he was fascinated by theories such as supersymmetry, supergravity, superspace and string theory. In his original works, as well as in various review articles, he has always associated the concepts of symmetry with the ideas of harmony and regularity. These ideas, underlying his search for patterns of unification of the fundamental forces of Nature, were aimed at obtaining a grandiose vision of the physical world, and remain among the most fascinating aspects of his scientific adventure. The Standard Model of electroweak interactions, formulated by Abdus Salam, Steven Weinberg and Sheldon Glashow (the first stage achieved so far on the path towards the unification of the fundamental interactions of nature) has significantly guided CERN's scientific programs throughout its history and - through CERN - the entire development of High Energy Physics. References ➡️https://t.co/iGFBlm4D6Q ➡️https://t.co/staVCFa57I #scritturebrevi #ventaglidiparole #Science #History

January 29, 1926: the great theoretical physicist Abdus Salam was born in the Punjab Province of British India (now in Pakistan).

Fri, 11 Sep 2026 21:01:23 GMT

MIT's "Topics in Mathematics with Applications in Finance"

Fri, 11 Sep 2026 20:54:06 GMT

MIT's "Topics in Mathematics with Applications in Finance" Lecture Notes: https://t.co/WiecmTrW09 Videos: https://t.co/E4Z2fJh6A6 https://t.co/PU2HxNCE66

Quantum Physics Full Course | Quantum Mechanics Course

Fri, 11 Sep 2026 20:54:01 GMT

Quantum physics also known as Quantum mechanics is a fundamental theory in physics that provides a description of the physical properties of nature at the scale of atoms and subatomic particles. It is the foundation of all #quantum #physics including quantum chemistry, quantum field theory, quantum technology, and quantum information science. In this course you will learn about Quantum #mechanics from the beginning to the end. The following topics of Quantum mechanics have been discussed in this course: ⭐️ Table of Contents ⭐️ ⌨️ (0:00:00) Introduction to quantum mechanics ⌨️ (0:16:23) The domain of quantum mechanics ⌨️ (0:24:18) Key concepts of quantum mechanics ⌨️ (0:34:04) A review of complex numbers for QM ⌨️ (0:48:12) Examples of complex numbers ⌨️ (1:01:47) Probability in quantum mechanics ⌨️ (1:12:17) Variance of probability distribution ⌨️ (1:26:16) Normalization of wave function ⌨️ (1:51:47) Position, velocity and momentum from the wave function ⌨️ (2:10:59) Introduction to the uncertainty principle ⌨️ (2:24:32) Key concepts of QM - revisited ⌨️ (2:37:45) Separation of variables and Schrodinger equation ⌨️ (3:09:55) Stationary solutions to the Schrodinger equation ⌨️ (3:15:47) Superposition of stationary states ⌨️ (3:25:37) Potential function in the Schrodinger equation ⌨️ (3:48:10) Infinite square well (particle in a box) ⌨️ (4:00:58) Infinite square well states, orthogonality - Fourier series ⌨️ (4:08:07) Infinite square well example - computation and simulation ⌨️ (4:39:27) Quantum harmonic oscillators via ladder operators ⌨️ (5:16:48) Quantum harmonic oscillators via power series ⌨️ (5:28:32) Free particles and Schrodinger equation ⌨️ (5:34:37) Free particles wave packets and stationary states ⌨️ (6:10:33) Free particle wave packet example ⌨️ (6:13:43) The Dirac delta function ⌨️ (6:20:49) Boundary conditions in the time independent Schrodinger equation ⌨️ (6:24:39) The bound state solution to the delta function potential TISE ⌨️ (6:43:29) Scattering delta function potential ⌨️ (6:55:49) Finite square well scattering states ⌨️ (7:07:39) Linear algebra introduction for quantum mechanics ⌨️ (7:10:34) Linear transformation ⌨️ (7:13:04) Mathematical formalism is Quantum mechanics ⌨️ (7:37:52) Hermitian operator eigen-stuff ⌨️ (8:01:23) Statistics in formalized quantum mechanics ⌨️ (8:24:26) Generalized uncertainty principle ⌨️ (8:54:36) Energy time uncertainty ⌨️ (9:16:33) Schrodinger equation in 3d ⌨️ (9:19:56) Hydrogen spectrum ⌨️ (9:31:14) Angular momentum operator algebra ⌨️ (9:57:17) Angular momentum eigen function ⌨️ (10:18:08) Spin in quantum mechanics ⌨️ (10:22:23) Two particles system ⌨️ (10:58:03) Free electrons in conductors ⌨️ (11:09:23) Band structure of energy levels in solids ⭐️ Credit ⭐️ Course Author: Brant Carlson Website: https://www.youtube.com/channel/UCNIEAv633WRg4ubBIhOcwMg ⭐️ Join Us ⭐️ Join our FB Group: https://www.facebook.com/groups/cslesson Like our FB Page: https://www.facebook.com/cslesson/ Website: https://cslesson.org/

Lecture Series on Quantum Mechanics (Beginner to Advanced)

Fri, 11 Sep 2026 20:54:01 GMT

Quantum mechanics is a branch of physics that deals with the behavior of matter and energy at the quantum level, which is the smallest scale of energy and matter that exists. This field of study has revolutionized our understanding of the physical world, and has many real-world applications in fields like computing, cryptography, and medical imaging. I am starting a lecture series on Quantum Mechanics, and this is the very first video of that series. In this video I give an introduction to the subject of QM, discuss the Syllabus of the subject , and also discuss some of the difficulties students face while studying this subject. 00:00 Introduction 03:15 Syllabus of QM 09:52 Difficulties faced by Students 17:24 Additional Information Playlist on the Lecture Series of QM https://youtube.com/playlist?list=PLRN3HroZGu2mCtdalEmZAM2nr1xBWAtUn 𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬MY NOTES - GDRIVE𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬 Find the PDF Scanned copy of my NOTES for this lecture here: https://drive.google.com/drive/folders/1-nJROXYt5whHxTQAPlUfMZbozGf1xUv9?usp=drive_link Join my Telegram Channel ► https://t.me/FortheLoveofPhysicsYT 𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬 Your financial support provides me an additional incentive to create high quality lecture videos. I am very much thankful for your generosity and kindness Support in Patreon ❤️❤️❤️https://www.patreon.com/dibyajyotidas Donate in Paypal 🔥🔥🔥 https://paypal.me/FortheLoveofPhysics JOIN as a member in Youtube 😇😇😇 https://www.youtube.com/channel/UCOfLm6gZGt3vwTMKRg-irhg/join 𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬𓏬

Transformers, the tech behind LLMs | Deep Learning Chapter 5

Fri, 11 Sep 2026 20:49:32 GMT

Breaking down how Large Language Models work, visualizing how data flows through. Instead of sponsored ad reads, these lessons are funded directly by viewers: https://3b1b.co/support --- Here are a few other relevant resources Build a GPT from scratch, by Andrej Karpathy https://youtu.be/kCc8FmEb1nY If you want a conceptual understanding of language models from the ground up, @vcubingx just started a short series of videos on the topic: https://youtu.be/1il-s4mgNdI?si=XaVxj6bsdy3VkgEX If you're interested in the herculean task of interpreting what these large networks might actually be doing, the Transformer Circuits posts by Anthropic are great. In particular, it was only after reading one of these that I started thinking of the combination of the value and output matrices as being a combined low-rank map from the embedding space to itself, which, at least in my mind, made things much clearer than other sources. https://transformer-circuits.pub/2021/framework/index.html History of language models by Brit Cruise, @ArtOfTheProblem https://youtu.be/OFS90-FX6pg An early paper on how directions in embedding spaces have meaning: https://arxiv.org/pdf/1301.3781.pdf Звуковая дорожка на русском языке: Влад Бурмистров. --- Timestamps 0:00 - Predict, sample, repeat 3:03 - Inside a transformer 6:36 - Chapter layout 7:20 - The premise of Deep Learning 12:27 - Word embeddings 18:25 - Embeddings beyond words 20:22 - Unembedding 22:22 - Softmax with temperature 26:03 - Up next

Eigenvectors and eigenvalues | Chapter 14, Essence of linear algebra

Fri, 11 Sep 2026 20:49:32 GMT

A visual understanding of eigenvectors, eigenvalues, and the usefulness of an eigenbasis. Help fund future projects: https://www.patreon.com/3blue1brown An equally valuable form of support is to simply share some of the videos. Home page: https://www.3blue1brown.com Full series: https://3b1b.co/eola Future series like this are funded by the community, through Patreon, where supporters get early access as the series is being produced. http://3b1b.co/support A solution to the puzzle at the end: https://www.dropbox.com/s/86yddvprfuaafju/Eigenvalue%20puzzle%20solution.pdf?dl=0 Typo: At 12:27, "more that a line full" should be "more than a line full". Thanks to these viewers for their contributions to translations Hebrew: Omer Tuchfeld ------------------ 3blue1brown is a channel about animating math, in all senses of the word animate. Various social media stuffs: Website: https://www.3blue1brown.com Twitter: https://twitter.com/3Blue1Brown Patreon: https://patreon.com/3blue1brown Facebook: https://www.facebook.com/3blue1brown Reddit: https://www.reddit.com/r/3Blue1Brown

Vectors | Chapter 1, Essence of linear algebra

Fri, 11 Sep 2026 20:49:32 GMT

Beginning the linear algebra series with the basics. Help fund future projects: https://www.patreon.com/3blue1brown Music: https://vincerubinetti.bandcamp.com/track/grants-etude Thanks to Elo Marie Viennot and Ambros Gleixner from HTW Berlin (www.htw-berlin.de) for contributing German translations and dubbing. Thanks also to these viewers for their contributions to the subtitles Arabic: @Cewkins, Hazem Bengali: Md Rasheduzzaman Czech: @Chnapak Greek: Nikolaos Tsagkas Hebrew: Omer Tuchfeld Italian: mattiacolucci Spanish: Juan Carlos Largo Thai: own doggoV●ᴥ●V ------------------ 3blue1brown is a channel about animating math, in all senses of the word animate. And you know the drill with YouTube, if you want to stay posted about new videos, subscribe, and click the bell to receive notifications (if you're into that). If you are new to this channel and want to see more, a good place to start is this playlist: https://goo.gl/WmnCQZ Various social media stuffs: Website: https://www.3blue1brown.com Twitter: https://twitter.com/3Blue1Brown Patreon: https://patreon.com/3blue1brown Facebook: https://www.facebook.com/3blue1brown Reddit: https://www.reddit.com/r/3Blue1Brown