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Artificial Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep Learning
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.
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.
Kindle Edition
Published March 29, 2020
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Displaying 1 - 2 of 2 reviews
July 2, 2020
Very good survey of the recent developments in artificial intelligence. It's a textbook... but it does not feel like it, don't let that disway you. Stone writes with an informal style making freehand analogies that help the reader to understand technical ideas. I had almost no AI knowledge going in and understood most of the content. As long as you have a strong number sense for sums and series you should be well equipped to read.
September 5, 2024
Good, fairly mathematical overview of the fundamental ideas behind the major concepts in neural networks and machine learning. Readable and well-written.
Displaying 1 - 2 of 2 reviews