Chinglong @ChinglongSn

Harvard's "Advanced Calculus" by Lynn H. Loomis & Shlomo Sternberg PDF: people.math.harvard.edu/~shlomo/docs/A…

"Problem-Solving Strategies" by Arthur Engel One of the most popular books among students preparing for math competitions. archive.org/details/Proble…

MIT's "Introduction to Algorithms" Lecture Notes: ocw.mit.edu/courses/6-006-… Lecture Videos: youtube.com/playlist?list=…

MIT's "Introduction to Computational Thinking and Data Science" Slides: ocw.mit.edu/courses/6-0002… Videos: ocw.mit.edu/courses/6-0002…

🚨 @3BLUE1BROWN DID IT AGAIN Language compressibility is not just a neat math trick: it is the core engine of modern LLMs. Grant's latest video boils Shannon's entropy down to a single, powerful idea: Prediction IS compression. → Predict the next word better, use fewer bits to store it → Shannon found English is astonishingly compressible (~1 bit per character) → This is the exact mechanism GPT models run on → Under this framing, intelligence equals compression FUN FACT: Von Neumann told Shannon to use the term "entropy" because no one really understands it. Today, it powers the AI revolution. Deep-dive resources in the 🧵↓

Over the past three years, Algebrica has been built on hundreds of books, lecture notes, papers, and other mathematical resources that I have read and consulted. I’m now putting all that material in order while I complete the review of the entries published so far. A bibliography is now available on GitHub. It will grow over time and, I hope, become a useful reference for anyone studying mathematics or looking for high-quality learning resources. You can find the first draft (...it's still in its infancy...) here: github.com/antoniolupetti…

"Linear Algebra Problem Book" by Paul R. Halmos "This is more than a problem book: it is a complete inquiry-based course in linear algebra" (from Allen Stenger's MAA review on this book) Archive: archive.org/details/linear… Stenger's review: old.maa.org/press/maa-revi…

10 free textbooks from MIT, Stanford, and Berkeley that you can download legally right now. → Introduction to Linear Algebra - Gilbert Strang, MIT The textbook behind the most-watched math course in history. 20 million views on OCW. Every ML engineer learned this math from one quiet professor. math.mit.edu/~gs/linearalge… → Mathematics for Computer Science - MIT 6.042 Proofs, discrete math, probability. The actual foundation of CS that nobody tells undergrads about until it's too late. ocw.mit.edu/courses/6-042j… → Convex Optimization - Stephen Boyd, Stanford Used in every serious ML and control systems course on earth. Cambridge University Press gave Boyd permission to keep it free on his own site. web. stanford. edu/~boyd/cvxbook/bv_cvxbook.pdf → CS229 Machine Learning Notes - Andrew Ng, Stanford Not the Coursera version. The actual Stanford graduate course notes. Dense, precise, and the closest thing to a grad school education you can download in one PDF. cs229.stanford.edu/main_notes.pdf → An Introduction to Statistical Learning - Stanford / USC The book three statisticians from Stanford and USC made free because they wanted everyone to learn it. 290,000 people have taken the companion course on edX. statlearning.com → Computational and Inferential Thinking - Berkeley Data 8 The textbook behind Berkeley's most popular course. Data science from scratch, built to be understood without a math degree first. inferentialthinking.com → Dive into Deep Learning - Berkeley / Amazon Jensen Huang called it "excellent." 500 universities across 70 countries use it. Every concept runs as live code directly in the browser. d2l.ai → Introduction to Probability - Blitzstein & Hwang, Harvard The official textbook of Harvard's Stat 110, which has been called the best probability course ever put on YouTube. Free second edition online. probabilitybook.net → The Elements of Statistical Learning - Hastie, Tibshirani, Friedman, Stanford The graduate-level version of ISLR. Springer makes it free as a PDF. Researchers keep a copy permanently in their downloads folder. hastie.su.domains/ElemStatLearn/ → MIT OCW Online Textbooks Index - 45+ books across every department One page. Every free MIT textbook organized by subject. Algorithms, physics, economics, engineering. All open access. ocw.mit.edu/courses/online… Save this before someone makes them take it down. (They won't. But save it anyway.)

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Peter Shor's Lecture Notes on Quantum Computing 📑 Lecture 1 --- Introduction and History Lecture 2 --- The Superposition Principle Lecture 3 --- Unitary Evolution and the Bloch Sphere Lecture 4 --- Quantum Measurements Lecture 5 --- Joint Quantum Systems and Tensor Products Lecture 6 --- More Tensor Products (Measurements of Joint Systems) Lecture 7 --- Classical Boolean circuits Lecture 8 --- Reversible Boolean circuits Lecture 9 --- Quantum gates I Lecture 10 --- Quantum gates II Lecture 11 --- Quantum Teleportation Lecture 12 --- Density Matrices I Lecture 13 --- Density Matrices II Lecture 14 --- The GHZ Experiment (theory) Lecture 15 --- Quantum Optics and the GHZ Experiment Lecture 16 --- The Deutsch-Jozsa Algorithm Lecture 17 --- Classical computational complexity theory Lecture 18 --- Simon's algorithm Lecture 19 --- The quantum Fourier transform Lecture 20 --- Phase Estimation Lecture 21 --- Quantum factoring algorithm Lecture 22 --- The Number Theory Needed for the Factoring Algorithm Lecture 23 --- The Discrete Log Algorithm Lecture 24 --- Grover's search algorithm Lecture 25 --- Proof that Grover Search is Optimal Lecture 26 --- Lecture on Hamiltonian Simulation; Notes Unwritten Lecture 27 --- Introduction to Quantum error correcting codes --- the 9-qubit code Lecture 28 --- More on the 9-qubit code Lecture 29 --- The 7-qubit Quantum Hamming Code Lecture 30 --- Quantum CSS Codes Lecture 31 --- The BB84 Quantum Key Distribution Protocol and the Proof of Its Security math.mit.edu/~shor/435-LN/

Robotics beginners unite! 😎 Here's a free course on the basics of robotics, including sensors and autonomous mobile bots. Everyone who studied robotics or EE knows @MATLAB very well. I've been using it for designing motors and robots back in the day. @MathWorks created a 17-video playlist teaching robotics using real robotic platforms, and other educational kits. Learn how to design, simulate, and control robots. They cover robot navigation with encoders, obstacle detection with IR sensors, vision-based autonomy, smart motor fine-tuning, virtual world simulation, and even modeling wheel-legged robots. Very good for someone just getting started and looking for a low entry-point course totally for free. Simulink modeling for control, Stateflow for state machines, hardware support packages, external mode debugging, virtual simulation before physical deployment. You can actually work on real robotics platforms! This is how you start in robotics! 🔗 Free YouTube vid library: youtube.com/playlist?list=… ~~ ♻️ Join the weekly robotics newsletter, and never miss any news → ziegler.substack.com

Wanna Learn Physics for Free? Here are some of the best freely available playlists from introductory Classical Mechanics to advanced topics like Quantum Mechanics and General Relativity. These are high-quality university-level lecture series from MIT, Stanford and Caltech:

Cornell's Robot Learning Course. Every slide. Every assignment. Free. 📌 CS 4756 covers the full modern stack: → Imitation learning → Reinforcement learning → Model predictive control → 3D perception → Sim-to-real transfer → LLMs for robot control Every lecture slide is publicly downloadable. All assignments are on GitHub. The course textbook is a live document, constantly updated by the authors. Currently taught by Kuan Fang; Cornell CS professor whose research focuses on getting robots to interact with the physical world in robust and dexterous ways. This is the curriculum the field is hiring for right now. Free. No login. 📌 [cs.cornell.edu/courses/cs4756…] Follow for more robotics resources!! ——- Weekly robotics and AI insights. Subscribe free: 22astronauts.com

🚀 Want to Learn AI for Free? Skip the expensive courses and learn directly from the companies building the future of AI. 🔹 Anthropic — anthropic.skilljar.com 🔹 Google — grow.google/ai 🔹 Meta — ai.meta.com/resources/ 🔹 NVIDIA — developer.nvidia.com/cuda 🔹 Microsoft — learn.microsoft.com/training 🔹 OpenAI — academy.openai.com 🔹 IBM — skillsbuild.org 🔹 AWS — skillbuilder.aws 🔹 DeepLearning.AI — deeplearning.ai 🔹 Hugging Face — huggingface.co/learn Whether you're a beginner, developer, student, or professional, these free resources can help you build practical AI skills from industry leaders. 📌 Save this post for later. 🔄 Repost to help others learn. 💬 Which platform are you starting with? #AI #ArtificialIntelligence #MachineLearning #DataScience #GenerativeAI #LLM #TechSkills #Learning #CareerGrowth #OpenAI

This 277-page PDF unlocks the secrets of Large Language Models. Here's what's inside: 🧵

Most people use NotebookLM the wrong way. They only ask for summaries — and end up with average results. Here are 10 advanced NotebookLM prompts that help you learn faster, think deeper, and truly understand your sources. 🔖 Save this for later.

University of Michigan is offering FREE courses 🔥 💻 Computer Science 🤖 Artificial Intelligence 🐍 Python ☁️ Cloud Computing 🔐 Cybersecurity 📊 Data Science 🗄️ SQL & Databases Here are 7 FREE courses to become skilled in 2026: Open this thread get started today 🧵👇

10 RESEARCH WEBSITES THAT PHDS DO NOT WANT YOU TO FIND. Bookmark this. Academia is gatekept by paywalls and you should not be paying. 1. annas-archive.org The largest open library on earth. Almost any textbook your professor assigned is here for free. 2. scholar.google.com The search engine for academic papers. Sort by citations to find the most influential research. 3. semanticscholar.org AI powered paper search built by the Allen Institute. Highlights every citation in context. 4. connectedpapers.com Plug in one paper, see every related study mapped as a graph. Reveals what experts actually read together. 5. elicit.com An AI research assistant. Ask any question and get a structured table of papers with key findings. 6. consensus.app Aggregates the conclusions of thousands of papers into one answer. Stops cherry picking. 7. researchrabbit.ai The Spotify of papers. Recommends new research based on what you have already read. 8. litmaps.com Visualizes citation chains. Shows how an idea spread across decades of research. 9. scite.ai Tells you which papers support, contradict, or mention any claim. Saves hours of fact checking. 10. core.ac.uk 200 million open access papers in one searchable index. The world's largest free academic archive. Most students pay $40,000 to access what these sites already make free.

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Stop learning ML from random tabs and half-finished playlists Machine Learning Course Notes is a public collection of lecture notes for machine learning, NLP, and AI courses. It helps you build a cleaner study path by grouping courses, lectures, videos, notes, descriptions, and authors in one scan-friendly table format. Key features: • Course-based map – sections for Machine Learning Specialization, MIT 6.S191, CMU Neural Nets for NLP, CS224N, CS25, and more • Lecture-level entries – each row shows the lecture name, description, video, notes, and author • Notes + video pairing – jump from course context to the original lecture video and written notes when available • WIP markers – incomplete notes are clearly labeled so you know what is ready and what is still being filled in • Contribution path – README explains how to pick a lecture, avoid duplicate work through issues, revise notes, and open a PR Free public GitHub repo; notes are licensed under CC BY-NC-SA 4.0. Link in the reply 👇

Structured learning roadmaps for specialized AI and data roles github.com/h9-tect/AI-Roa…

10 Websites Every Student Should Use Before Graduation. Bookmark every one of them. 1. notebooklm.google Upload every textbook, lecture, and PDF for a course. Ask questions across all of them. Built by Google DeepMind. 2. sci-bot.ru Search scientific research fast. Ask any question and get answers with links to relevant papers in seconds. 3. annas-archive.gl The world's largest open library. Almost any textbook your professor assigned is on here for free. 4. perplexity.ai Research assistant that cites every source. Replaces 90% of Google searches for academic work. 5. zotero.org Free reference manager that builds your bibliography automatically. Saves 20+ hours per semester. 6. wolframalpha.com Solves math, physics, chemistry, and engineering problems step by step. Shows the full working. 7. handshake.com The job platform built specifically for students. 1.4 million employers actively recruiting on it right now. 8. fastweb.com Matches you to scholarships you actually qualify for. Over $3.4 billion awarded to students every year. 9. coursera.org/learn/learning… Free Barbara Oakley course taken by 4 million people. The science of how to actually study and remember. 10. linkedin.com/learning Free with most university logins. 16,000+ courses on everything from Excel to AI engineering. Success after graduation isn’t always about being smarter. Often, it’s about finding and using the right tools before everyone else.