Artificial Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep Learning

Artificial Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep Learning

by James V Stone


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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 (e.g. perceptrons), and modern deep neural networks (e.g. generative adversarial networks). 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), and a list of further readings, this is an ideal introduction to the algorithmic engines of modern artificial intelligence.

Product Details

ISBN-13: 9780956372819
Publisher: Sebtel Press - NH
Publication date: 04/01/2019
Pages: 218
Sales rank: 587,293
Product dimensions: 6.00(w) x 9.00(h) x 0.46(d)

Table of Contents

List of Pseudocode Examples

Online Code Examples


1. Artificial Neural Networks

2. Linear Associative Networks

3. Perceptrons

4. The Backpropagation Algorithm

5. Hopfield Nets

6. Boltzmann Machines

7. Deep RBMs

8. Variational Autoencoders

9. Deep Backprop Networks

10. Reinforcement Learning

11. The Emperor’s New AI?

Further Reading


A Glossary

B Mathematical Symbols

C A Vector Matrix Tutorial

D Maximum Likelihood Estimation

E Bayes’ Theorem


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