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.
“I failed my way to success” — Thomas Edition.
Emoji notations: 🐙 (Github), 📚 (Book / Files), 🟢 (I’ve noted/learned), 📺 (Youtube/Videos), ⭐ (My favorite), 🎓 (MOOC / Courses)
- ⭐ 3Blue1Brown 📺
- ⭐ Andrej Karpathy 📺 (Founding member of OpenAI)
- https://github.com/faridrashidi/kaggle-solutionskaggle-solutions — Collection of Kaggle Solutions and Ideas 🐙
- Understanding AI Models - IBM Technology 📺
- A Mind for Numbers - Dr. Barbara Oakley 📚
- How to Read a Book - Mortimer Adler 📚
- Learning How To Learn - Dr. Barbara Oakley 📚
- Learning How to Learn: Powerful mental tools to help you master tough subjects — taught by Dr. Barbara Oakley (the author of books “Learning How To Learn” and “A Mind for Numbers”) 🎓
- Make It Stick by Brown, Roediger & McDaniel 📚
- Pragmatic Thinking and Learning: Refactor Your Wetware — Andy Hunt 📚
- Reading research papers by Andrew Ng 📺
- Understanding How We Learn: A Visual Guide — Yana Weinstein, Megan Sumeracki, Oliver Caviglioli 📚
- ⭐ Andrej Karpathy (his wonderful youtube channel 📺)
- serrano.academy 📺 (Youtube channel of Luis Serrano, the author of Grokking Machine Learning)
- sentdex 📺
- vcubingx 📺 (A Youtube channel like 3Blue1Brown, talk about Math and CS).
- https://github.com/faridrashidi/kaggle-solutionskaggle-solutions — Collection of Kaggle Solutions and Ideas 🐙
- ⭐ LeetCode
- ⭐ CS50x 2025 - CS Course from Harvard University, taught by David J. Malan 📺
- Self-taught DSA materials by YK Sugi (worked at Google, CS Dojo), sorted from beginning to advanced
- Algorithms Specialization - Stanford 🎓
- Algorithms, Fourth Edition by Robert Sedgewick and. Kevin Wayne 📚
- Introduction to data structures - myucodeschool 📺
- Learn Data Structures and Alghorithms - Udacity 🎓
- MIT 6.006 Introduction to Algorithms, Fall 2011 🎓
- The Algorithm Design Manual book by Steven S. Skiena 📚
- Designing Data-Intensive Applications by Martin Kleppmann 📚
- Introduction to Machine Learning Systems by Vijay Janapa Reddi (Harvard U.) 📚
- Convolutions in Image Processing | Week 1, lecture 6 | MIT 18.S191 Fall 2020 (taught by the author of 3Blue1Brown) 📺 🎓
Articles
- [PDF] A guide to convolution arithmetic for deep learning by Vincent Dumoulin and Francesco Visin (animations) 📚
- [Jupyter Notebook] data-science-ipython-notebooks 🐙
- Entropy (for data science) Clearly Explained!!! 📺 🟢 Noted in Goodnotes
- CS 152: Neural Networks/Deep Learning—Spring, 2021 | Video series taught by Neil Rhodes (his video about ResNet is really good) 🎓
- Deep Learning by Ian Goodfellow and Yoshua Bengio and Aaron Courville 📚
- Deep Learning Course (NYU, Spring 2020), taught by Yann LeCun 📺 🎓
- EfficientML.ai Lecture, Fall 2023, MIT 6.5940 📺 (Efficient Deep Learning Computing)
- Grokking Deep Learning - Andrew W. Trask
- Math and Architectures of Deep Learning by Krishnendu Chaudhury (notebooks) 📚
- Neural Networks: Zero To Hero by Andrej Karpathy, and these are the videos. 📺
- Neural networks and deep learning by Michael Nielsen — this book is recommended by 3Blue1Brown in his video about Neural Networks. 📚
- Understanding Deep Learning by Simon J.D. Prince 📚
- The Little Book of Deep Learning by François Fleuret (the author of Keras) 📚
- VIP cheatsheets for Stanford's CS 230 Deep Learning by Afshine Amidi & Shervine Amidi 📚 🐙
Articles
- Backprop Explainer (wonderful article with interactive elements)
- Deep Learning: An Introduction for Applied Mathematicians by Catherine F. Higham, Desmond J.Higham.
- Designing Data-Intensive Applications by Martin Kleppmann 📚
- The Complete Hands-On Introduction to Apache Airflow | Udemy by Marc Lamberti (His Youtube Channel 📺 is good as well)
- AI Engineering: Building Applications with Foundation Models | Chip Huyen 📚
- Awesome LLM Apps 🐙 — Collection of awesome LLM apps with AI Agents and RAG using OpenAI, Anthropic, Gemini and opensource models.
- Build LLM Applications (from Scratch) | Manning 📚
- Building Generative AI Services with FastAPI 📚
- Foundations of Large Language Models — Tong Xiao and Jingbo Zhu 📚
- Generative AI for Everyone | Coursera (taught by Andrew Ng) 🎓
- Generative AI for Beginners - Microsoft 🎓 🐙
- Hands-On Large Language Models: Language Understanding and Generation by Jay Alammar, Maarten Grootendorst 📚 — Github repository. 🐙
- ⭐ Large Language Models explained briefly - 3Blue1Brown 📺
- LLM courses 🐙 🎓 — everything to learn about LLM.
- Most devs don’t understand how context windows work - Matt Pocock 🐙
- ⭐ Neural networks | 3Blue1Brown 📺 (Chap 5 & 6 talk about GPT)
- Groking Machine Learning - Luis G. Serrano
- Machine Learning - A Probabilistic Perspective - Kevin P. Murphy 📚
- Machine Learning Specialization [3 courses] (Stanford) | Coursera 🟢 My notes for the old version of this course (using Matlab instead of Python). 🎓
- Pattern Recognition and Machine Learning - Christopher M. Bishop 📚
Elements of Statistical Learning: data mining, inference, and prediction. 2nd Edition. (use for ref, hard to read for self-study). Consider An Introduction to Statistical Learning instead (same authors). 📚
- Mathematics for Machine Learning and Data Science Specialization | DeepLearning.AI — taught by Luis Serrano. 🎓
- How not to be wrong by Jordan Ellenberg 📚 🟢
- Practical Statistics for Data Scientists by Andrew Bruce, Peter Bruce, and Peter Gedeck 📚
- Practical Statistics for Data Scientists by Andrew Bruce and Peter Bruce 📚
- ⭐ Seeing Theory 📚
- Statistics for Non-Statisticians by Birger Stjernholm Madsen 📚
- Think Bayes by Allen B. Downey 📚
- Think Stats by Allen B. Downey 📚
- Natural Language Processing by Yannic Kilcher (explain important papers in NLP) 🐙
- ⭐ Corey Schafer 📺
- ⭐ Lightning.ai, Google Colab 🟢 My note.
- Ray Fernando — 12y ex-Apple • I build AI apps live (bugs included) 📺
- Tools
- spec-kit — Toolkit to help you get started with Spec-Driven Development 🐙