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Convolutional neural network

Convolutional neural network

A type of neural network which uses overlapping input neurons modeled on the behavior of human visual cortex. Convolutional neural networks are best known for their use in image analysis, specifically object recognition.

A convolutional neural network (CNN, ConvNet) is a special kind of neural network that has been applied to a variety of pattern recognition problems, such as computer vision, speech recognition and others.

The architecture of a CNN is designed to take advantage of the 2D structure of an input image or other 2D input such as a speech signal. Unlike a regular neural network, CNN is comprised of one or more convolutional layers and then followed by one or more fully connected layers as in a standard multilayer neural network.

A CNN is has layers to perform four types of operation:

  • Convolution
  • Rectification (RELU)
  • Pooling or Sub Sampling - reduction of the dimensionality of each feature and retaining the most important information
  • Classification (Fully Connected Layer) to yield final class output

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Patrice Y. Simard, Dave Steinkraus and John C. Platt

Best Practices for Convolutional Neural NetworksApplied to Visual Document Analysis

Academic paper

Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner

Gradient-Based Learning Applied to Document Recognition

Academic paper

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