A comprehensive review of David Foster's Generative Deep Learning book, covering its strengths, weaknesses, and practicality for readers with different levels of ML knowledge.
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I read the first part of the book and very impressed by its explanation. However, keep in mind that the reader should be familiar with the basics of neural network
This book provides a great motivation behind studying generative models. I have found the explanations to be clear and helpful even on topics I am already familiar with.
David Foster's Generative Deep Learning book was the only reason I was able to get my dream job as a Machine Learning Engineer (even though I have no college education...I cannot express my gratitude đ)
This book is a really fun read with great examples in what;s going on with not only GANs, but also their relation to their variational autoencoder cousins. The examples are fantastic and the book is written incredibly well.
Paper is super thin and images seem to have printed by a cheap printer in economy mode.
I'm not usually picky about these things but it is very noticeable.
Content wise is OK, I especially liked the explanation about mode collapse in GANs and justification of WGAN losses and why they work. There are many analogies to explain concepts (VAEs, GANs, etc) and but I find some of them could be better.
I am half way through the book, and I would give 5 stars to this book. I like the examples that David used to describe VAE and GAN. David is a great writer who balances technical depth and lucid examples to illustrate the key concepts. I would strongly encourage David to write technical books in the near future.
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