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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 convolutional neural network can perform the following operations:

  • 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



Timeline

People

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Alex Krizhevsky





Yann LeCun





Further reading

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Author
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Best Practices for Convolutional Neural NetworksApplied to Visual Document Analysis

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

Academic paper



Convolutional Networks

Ian Goodfellow, Yoshua Bengio, Aaron Courville

Book Chapter



Going Deeper with Convolutions

Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich

Academic Paper



Gradient-Based Learning Applied to Document Recognition

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

Academic paper



ImageNet Classification with Deep Convolutional Neural Networks

Alex Krizhevsky, Ilya Sutskever, Geoffrey E Hinton

Academic Paper



Documentaries, videos and podcasts

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