convolutional neural nets

(1.3 hours to learn)


Convolutional neural networks are a kind of feed-forward neural net architecture geared towards visual processing. In each layer, there are several groups of units whose weights are repeated (or "shared") across all spatial locations. The forward pass and backpropagation updates can both be computed efficiently using convolution.


This concept has the prerequisites:

Core resources (read/watch one of the following)


Coursera: Neural Networks for Machine Learning (2012)
An online course by Geoff Hinton, who invented many of the core ideas behind neural nets and deep learning.
  • Lecture "Convolutional nets for digit recognition"
  • Lecture "Convolutional nets for object recognition"
Author: Geoffrey E. Hinton
Gradient-based learning applied to document recognition
Authors: Yann LeCun,Leon Bottou,Yoshua Bengio,Patrick Haffner

Supplemental resources (the following are optional, but you may find them useful)


See also