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.
- Andrej Karpathy - YouTube (Founding member of OpenAI)
- serrano.academy (Youtube channel of Luis Serrano, the author of Grokking Machine Learning)
- vcubingx (A Youtube channel like 3Blue1Brown, talk about Math and CS).
- Andrej Karpathy (his wonderful youtube channel)
[Book] PythonDataScienceHandbook
[Jupyter Notebook] data-science-ipython-notebooks
Entropy (for data science) Clearly Explained!!! - YouTube 👈 Noted in Goodnotes
[Book] Deep Learning with Python
Neural Networks: Zero To Hero by Andrej Karpathy, and these are the videos.
EfficientML.ai Lecture, Fall 2023, MIT 6.5940 - YouTube (Efficient Deep Learning Computing)
[Book] Deep Learning by Ian Goodfellow and Yoshua Bengio and Aaron Courville
[Book] Neural networks and deep learning by Michael Nielsen — this book is recommended by 3Blue1Brown in his video about Neural Networks.
CS 152: Neural Networks/Deep Learning—Spring, 2021 | Video series taught by Neil Rhodes (his video about ResNet is really good).
[Book] Understanding Deep Learning by Simon J.D. Prince
[Book + notebooks] Math and Architectures of Deep Learning by Krishnendu Chaudhury
[article] Deep Learning: An Introduction for Applied Mathematicians by Catherine F. Higham, Desmond J.Higham.
Backprop Explainer (wonderful article with interactive elements)
[Book] 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)
Generative AI for Everyone | Coursera (taught by Andrew Ng)
LLM courses | Github — everything to learn about LLM.
Generative AI for Beginners - Microsoft
[Book] Hands-On Large Language Models: Language Understanding and Generation by Jay Alammar, Maarten Grootendorst
[Book] Build LLM Applications (from Scratch) | Manning
[Book] AI Engineering: Building Applications with Foundation Models | Chip Huyen
[Book] Building Generative AI Services with FastAPI
Neural networks | 3Blue1Brown - YouTube (Chap 5 & 6 talk about GPT)
Mathematics for Machine Learning and Data Science Specialization | DeepLearning.AI — taught by Luis Serrano.
[Book] 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).
How not to be wrong by Jordan Ellenberg.
[Digital book] Seeing Theory
[Book] Practical Statistics for Data Scientists by Andrew Bruce, Peter Bruce, and Peter Gedeck
[Book] Think Stats by Allen B. Downey
[Book] Think Bayes by Allen B. Downey
[Book] Practical Statistics for Data Scientists by Andrew Bruce and Peter Bruce
[Book] Statistics for Non-Statisticians by Birger Stjernholm Madsen
Machine Learning Specialization [3 courses] (Stanford) | Coursera 👈 My notes for the old version of this course (using Matlab instead of Python).
100-Days-Of-ML-Code | Github
Machine-Learning-Tutorials | Github
[Book & online articles] The Hitchhiker’s Guide to Python! — The Hitchhiker's Guide to Python (github)
Convolutions in Image Processing | Week 1, lecture 6 | MIT 18.S191 Fall 2020 - YouTube (taught by the author of 3Blue1Brown)
[PDF] A guide to convolution arithmetic for deep learning by Vincent Dumoulin and Francesco Visin (animations)