Chapter 3

Convolutional Neural Networks

This chapter introduces convolutional neural networks from first principles — the sliding window operation of convolution, the parameter efficiency of shared weights, pooling as strategic forgetting, and the full architecture that transforms raw pixels into class probabilities. We also derive the output dimension formula you will use constantly when debugging CNN architectures.

• 1. The Problem→
• 2. Convolution→
• 3. Convolutional Layers→
• 4. Pooling Layers→
• 5. Convolutional Neural Network→
• 6. A Peek Inside a CNN→