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Deep Learning from Scratch: Building with Python from First Principles

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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.