# Deep Learning: Chapter 9

# 9 Convolutional Networks

## 9.1 The Convolution Operation

## 9.2 Motivation

Three important ideas:

- sparse interactions
- parameter sharing
- equivariant representations

## 9.3 Pooling

## 9.4 Convolution and Pooling as an Infinitely Strong Prior

One key insight is that …

Another key insight is that …

## 9.5 Variants of the Basic Convolution Function

**stride**

**padding**

- valid
- same
- full

locally connected layer(unshared convolution)

Tiled convolution

There three operations - convolution, backprop from output to weight, and backprop from output to inputs - are sufficient to compute all the gradients needed to train any depth of feedforward convolutional network.

## 9.6 Structured Outputs

## 9.7 Data Types

## 9.8 Efficient Convolution Algorithms

## 9.9 Random or Unsupervised Features

There are three basic strategies for obtaining convolution kernels without supervised training.

- initialize them randomly
- unsupervised learning approach
- intermediate approach