Ian Goodfellow

This is apparently THE book to read on deep learning. Written by luminaries in the field - if you've read any papers on deep learning, you'll have encountered Goodfellow and Bengio before - and cutting through much of the BS surrounding the topic: like 'big data' before it, 'deep learning' is not something new and is not deserving of a special name. Networks with more hidden layers to detect higher-order features, networks of different types chained together in order to play to their strengths, graphs of networks to represent a probabilistic model.

The book is 150 pages of background (stats, linear algebra), 300 pages of applications, and 200 pages of research topics. The applications section is the meat of the book: the background you might already know, and the research topics are mostly of interest only to fellow machine learning researchers.

Take note that this is a theoretical book. I read it in tandem with [b:Hands-On Machine Learning with Scikit-Learn and TensorFlow|32899495|Hands-On Machine Learning with Scikit-Learn and TensorFlow|Aurélien Géron|https://images.gr-assets.com/books/1478536137s/32899495.jpg|53513052], almost chapter-for-chapter. The Scikit-Learn and Tensorflow example code, while only moderately interesting on its own, helped to clarify the purpose of many of the topics in the Goodfellow book.… (més)