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



  • 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

9.10 The Neuroscientifice Basis for Convolutional Networks

9.11 Convolutional Networks and the History of Deep Learning