Table of Contents

Chapter 1:

  • The Simplest Neural Network
  • Feed Forward
  • Calculating Error
  • Single Neuron Backpropagation

Chapter 2:

  • Mean Squared Error
  • Learning Speed
  • Activation vs Error

Chapter 3:

  • Bias
  • Activation Functions
  • More Backpropagation

Chapter 4:

  • Multiple Serial Neurons
  • Multiple Parallel Neurons
  • Multiple Neuron Backpropagation

Chapter 5:

  • Layered Neural Networks
  • Linear Algebra Optimizations

Chapter 6:

  • Verifying Our Network In Tensorflow

Get Updates About New Chapters!

Sign up below to be notified when new chapters are added!

Starting Simple

In Chapter 1, you build the smallest possible neural network alongside clear explanations for the math that makes it work.

Step by Step Formulas

Derive the formulas that make neural networks work and translate them into code. No shortcuts; all of the math is here and easy to follow.

Form Follows Function

Separate the required building blocks of neural networks from the optimizations that make them scale.

From the author

As I was first learning neural networking, I struggled to separate the math that's required
with the math that's an optimization. This book is a concise yet comprehensive explanation
of the math behind neural networking alongside its matching code.
- Adam Wulf