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).
Entropy (for data science) Clearly Explained!!! - YouTube 👈 Noted in Goodnotes
[Book] Deep Learning with Python
Neural Networks: Zero To Hero by Andrej Karpathy, and this is the videos.
[Book] Understand Deep Learning by Simon J.D Prince
[Book] Deep Learning by Ian Goodfellow and Yoshua Bengio and Aaron Courville
- 3Blue1Brown
Neural networks | 3Blue1Brown - YouTube 👈 Noted in Goodnotes
[Book] Neural networks and deep learning by Michael Nielsen — this book is recommended by 3Blue1Brown in his video about Neural Networks.
[article] Deep Learning: An Introduction for Applied Mathematicians by Catherine F. Higham, Desmond J.Higham.
[Book] Designing Data-Intensive Applications by Martin Kleppmann
llm-course
— everything to learn about LLM.Generative AI for Beginners - Microsoft
Neural networks | 3Blue1Brown - YouTube (Chap 5 & 6 talk about GPT) 👈 Noted in Goodnotes
[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).
[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
Machine Learning Specialization [3 courses] (Stanford) | Coursera 👈 My notes for the old version of this course.
Object Detection for Dummies | Lil'Log: part 1, part 2, part 3, part 4. 👈 Reading: Object Detection for dummies (Lilian Weng)
[PDF] A guide to convolution arithmetic for deep learning by Vincent Dumoulin and Francesco Visin (animations)