num_pages: 214
description: The brain has always had a fundamental advantage over conventional computers: it can learn. However a new generation of artificial intelligence algorithms in the form of deep neural networks is rapidly eliminating that advantage. Deep neural networks rely on adaptive algorithms to master a wide variety of tasks including cancer diagnosis object recognition speech recognition robotic control chess poker backgammon and Go at super-human levels of performance. In this richly illustrated book key neural network learning algorithms are explained informally first followed by detailed mathematical analyses. Topics include both historically important neural networks (perceptrons Hopfield nets Boltzmann machines and backpropagation networks) and modern deep neural networks (variational autoencoders convolutional networks generative adversarial networks and reinforcement learning using SARSA and Q-learning). Online computer programs collated from open source repositories give hands-on experience of neural networks and PowerPoint slides provide support for teaching. Written in an informal style with a comprehensive glossary tutorial appendices (e.g. Bayes' theorem maximum likelihood estimation) and a list of further readings this is an ideal introduction to the algorithmic engines of modern artificial intelligence.
language: English
title: Artificial Intelligence Engines
publisher: Sebtel Press