What people recommend, in their own words, with the receipt.
Co-founder and chief science officer of Hugging Face; writes about open models and where he thinks the field is wrong.
8 things
Artificial Intelligence: A Modern ApproachBook Stuart Russell and Peter Norvig
"Artificial Intelligence: A Modern Approach" by Stuart Russell and Peter Norvig (http://aima.cs.berkeley.edu/) is a great ressource for all pre-neural-network tools and methods.
A reading list he posted, linked from his homepage bio, of the books and courses he used to self-teach ML/NLP.
thomwolf.io ↗·2024-01-01
Machine Learning: A Probabilistic PerspectiveBook Kevin P. Murphy
"Machine Learning: A Probabilistic Perspective" by Kevin P. Murphy (https://www.cs.ubc.ca/~murphyk/MLbook/) is a great ressource to go deeper in the probabilistic approach and get a good exposure to Bayesian tools.
A reading list he posted, linked from his homepage bio, of the books and courses he used to self-teach ML/NLP.
thomwolf.io ↗·2024-01-01
Information Theory, Inference and Learning AlgorithmsBook David MacKay
"Information Theory, Inference and Learning Algorithms" by David MacKay (http://www.inference.org.uk/mackay/itila/book.html) is a little gem that explain propabilities and Information theory so clearly it's almost unbelievable.
A reading list he posted, linked from his homepage bio; he repeats this same recommendation in his 2024 update below in the list.
thomwolf.io ↗·2024-01-01
Reinforcement Learning: An IntroductionBook Richard S. Sutton and Andrew G. Barto
"Reinforcement Learning: An Introduction" by Richard S. Sutton and Andrew G. Barto (http://incompleteideas.net/book/the-book.html) is a great ressource to get an introductory exposure to Reinforcement Learning
A reading list he posted, linked from his homepage bio, of the books and courses he used to self-teach ML/NLP.
thomwolf.io ↗·2024-01-01
Deep LearningBook Ian Goodfellow, Yoshua Bengio, Aaron Courville
The "Deep Learning" Book by Ian Goodfellow, Yoshua Bengio and Aaron Courville (https://www.deeplearningbook.org/) is a good ressource to get a quick overview of the current tools.
A reading list he posted, linked from his homepage bio, of the books and courses he used to self-teach ML/NLP; updated in 2024 with post-transformer advice.
thomwolf.io ↗·2024-01-01
The Book of Why: The New Science of Cause and EffectBook Judea Pearl
"The Book of Why: The New Science of Cause and Effect" by Pearl, Judea is a good introduction to Causality (more accessible than the big "Causality: Models, Reasoning and Inference")
A reading list he posted, linked from his homepage bio, of the books and courses he used to self-teach ML/NLP.
thomwolf.io ↗·2024-01-01
Neural Network Methods in Natural Language ProcessingBook Yoav Goldberg
Yoav Goldberg's book on "Neural Network Methods in Natural Language Processing" (https://www.amazon.com/Language-Processing-Synthesis-Lectures-Technologies/dp/1627052984) is nice too (see also an older free version here https://arxiv.org/abs/1510.00726)
A reading list he posted, linked from his homepage bio, of the books and courses he used to self-teach ML/NLP.
thomwolf.io ↗·2024-01-01
Natural Language Processing with TransformersBook Lewis Tunstall, Leandro von Werra, Thomas Wolf
read our book on NLP and transformers. It predate ChatGPT but it's still super relevant and goes up to training a LLM at the end: https://www.oreilly.com/library/view/natural-language-processing/9781098136789/
His 2024 update to the reading list, advising people joining the field after transformers; the linked O'Reilly book is his own co-authored "Natural Language Processing with Transformers".
thomwolf.io ↗·2024-01-01
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