Introduction

A CNN consists of many neural network layers. Two different types of layers, convolutional and pooling, are typically alternated. The depth of each filter increases from left to right in the network. The last stage is typically made of one or more fully connected layers:

An example of Convolutional Neural Network as seen is https://commons.wikimedia.org/wiki/File:Typical_cnn.png

There are three key intuitions behind convnets: local receptive fields, shared weights, and pooling. Let's review them together.

Local receptive fields

If we want to preserve the spatial information typically found in images, then it is convenient to represent each image with a matrix of pixels. Then, a simple way to encode the local...