Free “Deep Learning” Textbook by Goodfellow and Bengio Now Finished
I spent a few weeks closely reading this book and I have to disagree with the majority here. I didn't like the book at all. And I am an advanced math geek.<p>My main issue is that the book tells you all about the different parameter tweaks, but passes little concrete wisdom to the reader. It doesn't distinguish between modeling assumptions, and it replaces very simple explanations of concepts with complicated paragraphs that I can't make sense of.<p>I think it boils down to something that I have been feeling and hearing a lot in the past few years: the statistical jargon is so overwhelming that the authors can't explain things clearly. I can point to many examples in this book that I feel are unnecessary stumbling blocks, but the fact is that I'll spend an hour or two discussing parts of this book with a room full of smart machine learning researchers, and at the end we'll all agree we don't understand the material better than we did at the start.<p>On the other hand, I'll read <i>research papers</i> that don't force the statistical perspective down the reader's throat (e.g. <a href="http://arxiv.org/abs/1602.04485v1" rel="nofollow">http://arxiv.org/abs/1602.04485v1</a>) and find them very easy to understand by comparison.<p>It might be a cultural difference, but I've heard this complaint enough from experts who straddle both sides of the computational/statistical machine learning divide that I don't think it's just me.