This book steps you through coding layers, backpropagation, optimization, the trainer for the neural net; all the little bits and pieces that you'll want to know in-depth. It is well-organized and clearly explained. The author comes back to frame ideas as computational graphs over and over, and that provides an anchor for the reader as concepts become more complex. You will apply your code to model data sets, many of them the "usual suspects" that everyone should know.
You will want to check the author's github for this book. In some later chapters, parts of the code are omitted from print in the book, but are present in the github repo. There are also some minor corrections and updates that have been made to the repo since the book was printed. This was an excellent resource for me.
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From the Publisher
From the Preface
If you’ve tried to learn about neural networks and deep learning, you’ve probably encountered an abundance of resources, from blog posts to MOOCs (massive open online courses, such as those offered on Coursera and Udacity) of varying quality and even some books—I know I did when I started exploring the subject a few years ago. However, if you’re reading this preface, it’s likely that each explanation of neural networks that you’ve come across is lacking in some way. I found the same thing when I started learning: the various explanations were like blind men describing different parts of an elephant, but none describing the whole thing. That is what led me to write this book.
These existing resources on neural networks mostly fall into two categories. Some are conceptual and mathematical, containing both the drawings one typically finds in explanations of neural networks, of circles connected by lines with arrows on the ends, as well as extensive mathematical explanations of what is going on so you can “understand the theory.”
Other resources have dense blocks of code that, if run, appear to show a loss value decreasing over time and thus a neural network “learning.”
Explanations like this, of course, don’t give much insight into “what is really going on”: the underlying mathematical principles, the individual neural network components contained here and how they work together, and so on.
What would a good explanation of neural networks contain? For an answer, it is instructive to look at how other computer science concepts are explained: if you want to learn about sorting algorithms, for example, there are textbooks that will contain:
- An explanation of the algorithm, in plain English
- A visual explanation of how the algorithm works, of the kind that you would draw on a whiteboard during a coding interview
- Some mathematical explanation of “why the algorithm works”
- Pseudocode implementing the algorithm
One rarely—or never—finds these elements of an explanation of neural networks side by side, even though it seems obvious to me that a proper explanation of neural networks should be done this way; this book is an attempt to fill that gap.
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Top reviews from the United States
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Reviewed in the United States on February 8, 2021
Reviewed in the United States on November 20, 2020
This book will teach you how to code neural networks - MLPs, ConvNets, RNNs with LTSM and GRU cells!
Reviewed in the United States on August 12, 2020
Reviewed in the United States on January 23, 2020
Top reviews from other countries
Dazhen
The book and Jupiter notebook comes with it is full of sloppy annotation and errors
Reviewed in Canada on October 31, 2022
Eli Emanuel
Introduction to the Mathematics, Codes (Python), as well as Theories and Concepts of Deep Learning.
Reviewed in Singapore on May 9, 2021