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Hands-On Machine Learning with Scikit-Learn and TensorFlow by Aurélien Géron

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Appendix E. Other Popular ANN Architectures

In this appendix we will give a quick overview of a few historically important neural network architectures that are much less used today than deep Multi-Layer Perceptrons (Chapter 10), convolutional neural networks (Chapter 13), recurrent neural networks (Chapter 14), or autoencoders (Chapter 15). They are often mentioned in the literature, and some are still used in many applications, so it is worth knowing about them. Moreover, we will discuss deep belief nets (DBNs), which were the state of the art in Deep Learning until the early 2010s. They are still the subject of very active research, so they may well come back with a vengeance in the near future.

Hopfield Networks

Hopfield networks were first introduced by W. A. Little in 1974, then popularized by J. Hopfield in 1982. They are associative memory networks: you first teach them some patterns, and then when they see a new pattern they (hopefully) output the closest learned pattern. This has made them useful in particular for character recognition before they were outperformed by other approaches. You first train the network by showing it examples of character images (each binary pixel maps to one neuron), and then when you show it a new character image, after a few iterations it outputs the closest learned character.

They are fully connected graphs (see Figure E-1); that is, every neuron is connected to every other neuron. Note that on the diagram the images are 6 × 6 pixels, ...

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