The Essential NLP, AI, and ML Classics Shared by Thom Wolf: A Self-Learner's Guide

Thom Wolf , a co-founder of Hugging Face , started his career in Physics rather than Computer Science. In 2015, Thom was attracted by the new ML/AI revolution as many of the methods were just re-branded statistical physics approaches. He started his online education and shared the list of resources he used.

Reading list

1. The "Deep Learning " by Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Provides a great overview of current deep learning techniques.

2. "Artificial Intelligence: A Modern Approach " by Stuart Russell and Peter Norvig. Covers all pre-neural-network tools and methods.

3. "Machine Learning: A Probabilistic Perspective " by Kevin P. Murphy. Covers probabilistic approach and Bayesian tools.

4. "Information Theory, Inference, and Learning Algorithms " by David MacKay. Provides a great explanation of probabilities and information theory.

5. "The Book of Why: The New Science of Cause and Effect " by Pearl, Judea. A good introduction to Causality.

6. "Reinforcement Learning: An Introduction " by Richard S. Sutton and Andrew G. Barto.

Natural Language Processing: three great resources

1. Kyunghyun Cho's lecture notes on "Natural Language Processing with Representation Learning".

2. Yoav Goldberg's book on "Neural Network Methods in Natural Language Processing " and its older free version .

3. Jacob Eisenstein's textbook on "Natural Language Processing."

Online courses

• Computational Probability and Inference (6.008.1x) from edX

• Probabilistic Graphical Models Specialization from Coursera

Also, check our article about Hugging Face 👇🏼

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