# A Collection of Learning Resources The resources listed in this note have not been verified yet. They are included so that they can be checked at a later time. The order is random. > “You do not rise to the level of your goals. You fall to the level of your systems.” – James Clear **Icon Legend:** - GitHub Repository - Book or Document - Learned or Noted - Video or YouTube - Favorite Resource - Course or MOOC --- ## General Resources - **[3Blue1Brown](https://www.youtube.com/c/3blue1brown)** — Visual math and neural network animations - **[500+ AI Projects with Code](https://github.com/ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code)** — Machine Learning, Deep Learning, CV & NLP projects - **[Adam Lucek](https://adamlucek.com/)** — Developer portfolio, GitHub and YouTube channel - **[Andrej Karpathy](https://www.youtube.com/@AndrejKarpathy)** — Deep Learning and Neural Networks lectures - **[AWS Machine Learning Blog](https://aws.amazon.com/blogs/machine-learning/)** — Official AWS blog for ML architecture & tutorials - **[Build Your Own X](https://github.com/codecrafters-io/build-your-own-x)** — Recreate programming technologies from scratch - **[DeepLearning.AI](https://www.deeplearning.ai/)** — Start or advance your career in AI - **[Deep Learning Drizzle](https://github.com/Deep-Learning-Drizzle/Deep-Learning-Drizzle)** — Curated list of high-quality Deep Learning courses - **[NVIDIA Deep Learning Institute](https://www.nvidia.com/en-us/training/)** — Hands-on AI and GPU training and certification - **[DeepTutor](https://github.com/HKUDS/DeepTutor)** — Multi-agent document interactive learning system - **[Developer Roadmaps - roadmap.sh](https://roadmap.sh/)** — Community-created developer learning roadmaps - **[Distill](https://distill.pub/)** — Clear and visual machine learning research articles - **[Friends Transcripts & Scripts](https://uncut-friends-scripts.herokuapp.com/)** — Transcripts and script collection for learning Conversational English - **[From 0 to Research Scientist Guide](https://github.com/Mr-TalhaIlyas/From-0-to-research-scientist-resources-guide)** — Self-study guide for AI research scientists - **[Google AI](https://ai.google/)** — Google AI tools, research, training and skills - **[Google Cloud Skills Boost](https://www.cloudskillsboost.google/)** — Google Cloud training, labs and certifications - **[Google Skills](https://skillshop.exceedlms.com/)** — Free training and certifications for Google Cloud - **[GoSeedUp Music](https://goseedup.com/)** — Background focus music for workspace & concentration - **[Hugging Face Learn](https://huggingface.co/learn)** — Open-source Machine Learning and NLP courses - **[Hướng Dẫn Tự Học Trí Tuệ Nhân Tạo](https://www.youtube.com/results?search_query=H%C6%B0%E1%BB%9Bng+D%E1%BA%A5n+T%E1%BB%B1+H%E1%BB%8Dc+Tr%C3%AD+Tu%E1%BB%87+Nh%C3%A2n+T%E1%BA%A1o)** — AI self-study guides in Vietnamese - **[Collection of Kaggle Solutions](https://github.com/faridrashidi/kaggle-solutions)** — Winning Kaggle solutions and competitive ideas - **[Kaggle Learn Tutorials](https://www.kaggle.com/learn)** — Micro-courses for Python, Data Viz, and Pandas - **[Microsoft Learn Training](https://learn.microsoft.com/)** — Microsoft official developer and cloud courses - **[OpenStax](https://openstax.org/)** — Free, peer-reviewed open textbooks for Math, Physics & CS - **[Understanding AI Models - IBM](https://www.youtube.com/@IBMTechnology)** — Concise visual explanations of AI technology - **[Welch Labs](https://www.youtube.com/@welchlabs)** — Math, Science, and Machine Learning animations --- ## Learn How to Learn - **[A Mind for Numbers](https://barbaraoakley.com/books/a-mind-for-numbers/)** — How to excel at math and science by Dr. Barbara Oakley - **[How To Learn Any Skill So Fast It Feels Illegal](https://www.youtube.com/watch?v=p60rN9JEapg)** — Actionable techniques for rapid skill acquisition - **[How to Read a Book](https://en.wikipedia.org/wiki/How_to_Read_a_Book)** — Classic guide to intelligent reading by Mortimer Adler - **[Learning How To Learn Book](https://barbaraoakley.com/)** — Guide for kids and teens on effective learning - **[Learning How to Learn Coursera](https://www.coursera.org/learn/learning-how-to-learn)** — Popular MOOC on mental tools and memory techniques - **[Make It Stick](https://www.retrievalpractice.org/make-it-stick)** — Science of successful learning through retrieval practice - **[Pragmatic Thinking and Learning](https://pragprog.com/titles/ahprag/pragmatic-thinking-and-learning/)** — Refactor your wetware and cognitive habits by Andy Hunt - **[Reading Research Papers by Andrew Ng](https://www.youtube.com/watch?v=733m6qBH-jI)** — Efficient workflow for digesting technical papers - **[Understanding How We Learn](https://www.learningscientists.org/book)** — Visual guide to cognitive psychology in learning --- ## Agentic AI - **[all-agentic-architectures](https://github.com/waseemh-dev/all-agentic-architectures)** — Collection of AI Agent system design patterns - **[Claude Certified Architect – Foundations](https://academy.anthropic.com/)** — Official Anthropic certification for AI architecture --- ## Blogs & Personal Websites - **[Andrej Karpathy Blog](https://karpathy.ai/)** — Insights on Deep Learning, LLMs, and AI systems - **[Brittany Chiang](https://brittanychiang.com/)** — Software Engineer portfolio & design resources - **[Chip Huyen Blog](https://huyenchip.com/)** — Machine Learning Systems Design and MLOps - **[Colah's Blog](https://colah.github.io/)** — Visual and intuitive essays on Neural Networks - **[Corey Schafer YouTube](https://www.youtube.com/c/CoreySchafer)** — High quality Python, Django & Git video tutorials - **[Denny's Blog](https://dennybritz.com/)** — Deep Learning research notes by Denny Britz - **[Josh W. Comeau](https://joshwcomeau.com/)** — Interactive CSS & Web Development tutorials - **[Lil'Log](https://lilianweng.github.io/)** — In-depth technical AI posts by Lilian Weng - **[Raúl Gómez Blog](https://raulgomez.biz/)** — Computer Vision and Deep Learning research - **[ruder.io](https://ruder.io/)** — NLP and Machine Learning research updates - **[serrano.academy](https://serrano.academy/)** — Friendly Machine Learning video tutorials - **[sentdex](https://www.youtube.com/c/sentdex)** — Python programming and Machine Learning tutorials - **[StatQuest with Josh Starmer](https://www.youtube.com/c/joshstarmer)** — Statistics and Machine Learning broken down step by step - **[Dinh Anh Thi Blog](https://dinhanhthi.com/)** — Data Science, Machine Learning & Software Engineering notes by Dinh Anh Thi - **[vcubingx](https://www.youtube.com/c/vcubingx)** — Visual Mathematics and Computer Science concepts - **[Yannic Kilcher](https://www.youtube.com/c/yannickilcher)** — AI paper explanations and machine learning news --- ## Coding Platforms - **[CodeChef](https://www.codechef.com/)** — Practical coding practice and competitive programming - **[Exercism](https://exercism.org/)** — Code practice and mentorship in 70+ programming languages - **[HackerRank](https://www.hackerrank.com/)** — Technical interview prep and skill assessments - **[Kaggle Solutions](https://github.com/faridrashidi/kaggle-solutions)** — Repository of competitive machine learning code - **[LeetCode](https://leetcode.com/)** — Standard platform for Data Structures & Algorithms practice --- ## Computer Science - **[CS Video Courses](https://github.com/developer-y/cs-video-courses)** — Comprehensive list of Computer Science video courses - **[coding-interview-university](https://github.com/jwasham/coding-interview-university)** — Complete CS study plan for software engineering jobs - **[CS50x 2025 - Harvard](https://cs50.harvard.edu/x/)** — Introduction to Computer Science by David J. Malan - **[roadmap.sh](https://roadmap.sh/)** — Visual learning paths for CS, DevOps, and Frontend - **[Learn to Program: Crafting Quality Code](https://www.coursera.org/learn/program-code)** — Software quality course by University of Toronto - **[Teach Yourself Computer Science](https://teachyourselfcs.com/)** — Curated guide for self-taught software engineers - **[The Art of Debugging](https://github.com/stas00/the-art-of-debugging)** — Comprehensive open book on software debugging - **[Visualize the Brrr](https://github.com/brrr-lang/brrr)** — Learn GPU hardware architecture interactively --- ## Data Structures & Algorithms - **[Abdul Bari Channel](https://www.youtube.com/c/abdul_bari)** — Clear and comprehensive lectures on Algorithms - **[Algorithms by Jeff Erickson](https://jeffe.cs.illinois.edu/teaching/algorithms/)** — Free open textbook on Algorithm design - **[Algorithms Part I](https://www.coursera.org/learn/algorithms-part1)** — Princeton course by Sedgewick & Wayne - **[Algorithms Part II](https://www.coursera.org/learn/algorithms-part2)** — Advanced algorithms course from Princeton - **[awesome-algorithms](https://github.com/tayllan/awesome-algorithms)** — Curated list of algorithm learning resources - **[UCSD DSA Specialization](https://www.coursera.org/specializations/data-structures-algorithms)** — 6-course specialization in Data Structures - **[PrincetonAlgorithms](https://github.com/kevin-wayne/algs4)** — Code repository for Algorithms 4th edition - **[Stanford Algorithms Specialization](https://www.coursera.org/specializations/algorithms)** — Stanford algorithm course taught by Tim Roughgarden --- ## Design Patterns & System Design - **[awesome-system-design-resources](https://github.com/mikeroyal/System-Design-Guide)** — Free system design interview study guide - **[Baymard Ecommerce UX Research](https://baymard.com/)** — Large-scale UX research & design best practices - **[Designing Data-Intensive Applications](https://dataintensive.net/)** — Industry-standard book on distributed systems by Kleppmann - **[Design Patterns (Gang of Four)](https://en.wikipedia.org/wiki/Design_Patterns)** — Classic reference book on object-oriented design patterns - **[Head First Design Patterns](https://www.oreilly.com/library/view/head-first-design/9781492078005/)** — Beginner-friendly visual guide to OOP patterns - **[Introduction to ML Systems - Harvard](https://github.com/minimaxir/personally-curated-ml-case-studies)** — Course on building production machine learning systems - **[python-patterns](https://github.com/faif/python-patterns)** — Collection of design patterns implemented in Python - **[Refactoring.guru](https://refactoring.guru/)** — Visual interactive guide to refactoring and design patterns - **[Snappy UI Optimization with useDeferredValue](https://joshwcomeau.com/react/use-deferred-value/)** — Deep dive into React performance optimization by Josh W. Comeau --- ## Deep Learning & Neural Networks - **[Awesome Deep Learning](https://github.com/ChristosChristofidis/awesome-deep-learning)** — Curated list of deep learning tutorials and papers - **[Deep Learning Specialization](https://www.coursera.org/specializations/deep-learning)** — Landmark 5-course series by Andrew Ng - **[DeepMind x UCL Lecture Series](https://www.youtube.com/playlist?list=PLqYmG7hTraZCDxZ44o4p3N5Anz3lLRVZF)** — Advanced lectures on AI and Reinforcement Learning - **[Dive into Deep Learning](https://d2l.ai/)** — Interactive book with code in PyTorch, JAX and NumPy - **[Deep Learning Book](https://www.deeplearningbook.org/)** — Definitive textbook by Goodfellow, Bengio & Courville - **[NYU Deep Learning Course](https://at757.github.io/NYU-Deep-Learning-Spring-2020/)** — Course taught by Turing Award winner Yann LeCun - **[fast.ai Practical Deep Learning](https://www.fast.ai/)** — Hands-on deep learning for coders - **[Neural Networks Series](https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi)** — Visual introduction to backpropagation by 3Blue1Brown - **[Neural Networks: Zero To Hero](https://karpathy.ai/zero-to-hero.html)** — Build micrograd and GPT from scratch with Andrej Karpathy - **[Understanding Deep Learning](https://udlbook.github.io/udlbook/)** — Comprehensive textbook by Simon J.D. Prince --- ## Generative AI & Large Language Models - **[AI Engineering: Foundation Models](https://huyenchip.com/)** — Building production applications with LLMs by Chip Huyen - **[Awesome LLM Apps](https://github.com/Shubhamshoo/awesome-llm-apps)** — LLM apps built with AI Agents and RAG architectures - **[Build LLM Applications from Scratch](https://www.manning.com/books/build-a-large-language-model-from-scratch)** — Step-by-step guide to training and building LLMs - **[ChatGPT Prompt Engineering](https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/)** — Short course by DeepLearning.AI and OpenAI - **[Generative AI for Beginners - Microsoft](https://github.com/microsoft/generative-ai-for-beginners)** — 18-lesson curriculum on GenAI by Microsoft - **[Hands-On Large Language Models](https://github.com/HandsOnLLMs/HandsOnLLMs)** — Code repository for language understanding models - **[Hướng Dẫn Sử Dụng Local AI Chi Tiết](https://github.com/)** — Detailed guide on running Local LLMs (Ollama, LM Studio) in Vietnamese - **[LLM Visualization](https://bbycroft.net/llm)** — Interactive 3D visualization of how GPT architecture works - **[LLMs-from-scratch](https://github.com/rasbt/LLMs-from-scratch)** — Build a Transformer LLM from scratch in PyTorch - **[Stanford CS229: LLMs Lecture](https://www.youtube.com/watch?v=9vM4p9NN0Ts)** — Building Large Language Models lecture series - **[Prompt Engineering Guide - Lil'Log](https://lilianweng.github.io/posts/2023-03-15-prompt-engineering/)** — Comprehensive technical reference on prompt techniques --- ## Machine Learning & Mathematics - **[100-Days-Of-ML-Code](https://github.com/Avik-Jain/100-Days-Of-ML-Code)** — 100-day study roadmap for machine learning algorithms - **[Automate the Boring Stuff with Python](https://automatetheboringstuff.com/)** — Practical Python programming guide for beginners by Al Sweigart - **[Hands-On Machine Learning (3rd Ed)](https://www.oreilly.com/library/view/hands-on-machine-learning/9781098125967/)** — Practical guide using Scikit-Learn, Keras & TensorFlow - **[Machine Learning Cơ Bản](https://machinelearningcoban.com/)** — Machine Learning fundamentals explained in Vietnamese - **[ML Specialization by Andrew Ng](https://www.coursera.org/specializations/machine-learning-introduction)** — Updated Stanford ML course series in Python - **[Pattern Recognition and Machine Learning](https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf)** — Classic foundational textbook by Christopher Bishop - **[Recommended Python Resources (fast.ai)](https://forums.fast.ai/)** — Fast.ai curated Python learning guide for Data Science - **[Seeing Theory](https://seeing-theory.brown.edu/)** — Visual interactive introduction to probability & statistics --- ## Developer Tools & AI Workflows - **[Connected Papers](https://www.connectedpapers.com/)** — Visual graph tool to explore academic research papers - **[Cursor AI Code Editor](https://www.cursor.com/)** — Next-generation AI-first code editor - **[Claude Code by Anthropic](https://docs.anthropic.com/claude/docs/claude-code)** — Agentic CLI tool for codebase interaction - **[Lightning.ai](https://lightning.ai/)** — Platform for building and training multi-GPU AI models - **[Vercel AI SDK](https://sdk.vercel.ai/)** — Library for building AI streaming web applications - **[spec-kit](https://github.com/github/spec-kit)** — Toolkit for Spec-Driven Software Development --- *This collection is maintained and updated periodically. Feel free to explore the links above to deepen your technical knowledge.*