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)
- 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).
- 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 📚
- 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 📚
- EfficientML.ai Lecture, Fall 2023, MIT 6.5940 📺 (Efficient Deep Learning Computing)
- 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 📚
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)
- Andrej Karpathy 📺 (Founding member of OpenAI)
- AI Engineering: Building Applications with Foundation Models | Chip Huyen 📚
- 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. 🐙
- LLM courses 🐙 — everything to learn about LLM.
- Neural networks | 3Blue1Brown 📺 (Chap 5 & 6 talk about GPT)
- Machine Learning Specialization [3 courses] (Stanford) | Coursera 🟢 My notes for the old version of this course (using Matlab instead of Python).
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 📚
- Statistics for Non-Statisticians by Birger Stjernholm Madsen 📚
- Think Bayes by Allen B. Downey 📚
- Think Stats by Allen B. Downey 📚