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.
First impressions:<p>1. It also covers "classical" artificial neural networks, i.e., things like backprop from before Hinton and others made breakthroughs for deep learning. This means you can start with this book even if you are new to ANNs. The later sections cover "real deep learning".<p>2. The language is great for beginners and users. You don't have to be an advanced math geek to follow everything. They seem to cover a fair amount of ground too, so its not dumbed down either.<p>3. I guess it covers most of the underlying theory and practical technicques but is implementation neutral. You should probably pick up a tutorial for your favorite implementation like Theano, TensorFlow, etc.<p>All in all, I like it a lot.
This looks interesting, can't wait to dig into it.<p>Another great great free online book on this topic:
<a href="http://neuralnetworksanddeeplearning.com/" rel="nofollow">http://neuralnetworksanddeeplearning.com/</a>
For anyone interested, Goodfellow is answering questions about the book at: <a href="https://www.reddit.com/r/MachineLearning/comments/4domnk/the_deep_learning_textbook_is_now_complete/" rel="nofollow">https://www.reddit.com/r/MachineLearning/comments/4domnk/the...</a>
I don't claim to have a solution, but these models of book monetisation really seem doomed. What are the chances that I will buy this book just because they made it artificially harder for me to download it? Probably a net negative.
Thanks for this. I'm currently re-learning statics/probabilities and linear algebra so your book will be useful in a few months down the line ;)
This looks great, any other recommendations for enjoyable reads on ml/stat learning?<p>ESL
ISLR
doing Bayesian data analysis w/ jags/Stan
bda3 - gelman
prob graphical models
Convex analysis - Boyd
adv data analysis from elem pov - shalizi<p>Trying to build out my library. I have a background in prob/stats/analysis and measure theory/linear algebra and also knowledge of algorithms and data structures at the advanced undergrad level, So I'm not too concerned about technical depth just want to enjoy a good technical expository and gain intuition.
Does anybody know how to make the book actually readable? <a href="http://i.imgur.com/C4rhclk.png" rel="nofollow">http://i.imgur.com/C4rhclk.png</a>
Can any practitioners / experts out there comment on the range of topics? For example, I understand the book to be introductory, and so the scope is likely somewhat limited. But how close does it get you to the ANNs currently in use, at least conceptually if not in complete detail? Thanks!
Athena (<a href="http://athenapdf.com/" rel="nofollow">http://athenapdf.com/</a>) does a phenomenal job at turning those HTML pages into convenient PDF files.
Don't quite get the complaints about it not being available in PDF. "We'll publish your book, and you can give it away for free as long as you make people click through to each chapter" is a much, much better deal than I would expect from a big publisher.