Collection of notebooks, implemented research papers and resources for anyone interested in machine learning and deep learning. Educational purposes only.
git clone https://github.com/martintmv-git/cookbook.git
cd cookbook
- The Hundred-Page Machine Learning Book by Andriy Burkov
- Machine Learning Engineering by Andriy Burkov
- Neural Networks and Deep Learning by Michael Nielsen
- Machine Learning Yearning by Andrew Ng (Available for free as a downloadable PDF)
- The Little Book of Deep Learning by François Fleuret
- Bayesian Methods for Hackers by Cameron Davidson-Pilon
- Pattern Recognition and Machine Learning by Christopher M. Bishop
- ISL with Python
- No bullshit guide to linear algebra by Ivan Savov
- No bullshit guide to math and physics by Ivan Savov
- Data Science from Scratch: First Principles with Python, Edition: 2 by Joel Grus
- Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
- Machine Learning with PyTorch and Scikit-Learn by Sebastian Raschka
- 3Blue1Brown - Essence of linear algebra
- 3Blue1Brown - Neural networks
- Mathematics for Machine Learning - Coursera
- Machine Learning Specialization - Coursera
- Algebra 1M
- Differential and Integral Calculus
- PyTorch for Deep Learning
- Andrej Karpathy - Intro to Large Language Models
- Zero to GPT - A Guide
- Andrej Karpathy - Let's build GPT: from scratch, in code, spelled out
- Andrej Karpathy - Let's build the GPT Tokenizer
- Andrej Karpathy - The spelled-out intro to neural networks and backpropagation: building micrograd
- NLP Workshop - Olaf Janssen
- LLM Course Repository
- Radu Mariescu-Istodor - Machine Learning Course in JavaScript
