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.

Tools & Services & References

General

Blog / Personal websites

  • serrano.academy (Youtube channel of Luis Serrano, the author of Grokking Machine Learning)
  • vcubingx (A Youtube channel like 3Blue1Brown, talk about Math and CS).

Data Science

Articles / Videos

Deep Learning / CNN

Neural Networks: Zero To Hero by Andrej Karpathy, and these are the videos.
[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

Articles

[article] Deep Learning: An Introduction for Applied Mathematicians by Catherine F. Higham, Desmond J.Higham.
Backprop Explainer (wonderful article with interactive elements)

Engineering / MLOps

[Book] Designing Data-Intensive Applications by Martin Kleppmann

AWS Certified Data Engineer (DEA-C01)

Generative AI / LLM

LLM courses | Github — everything to learn about LLM.
[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

Articles / Video

Neural networks | 3Blue1Brown - YouTube (Chap 5 & 6 talk about GPT)

Maths / Prop / Stats

[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).

Statistics

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

Machine Learning Specialization [3 courses] (Stanford) | Coursera 👈 My notes for the old version of this course (using Matlab instead of Python).
A visual introduction to machine learning: Part 1, Part 2

NLP

Articles

Python

Pytorch

Tensorflow

Computer Vision

Articles

Object Detection for Dummies | Lil'Log: part 1, part 2, part 3, part 4.