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Artificial Intelligence Engines
A Tutorial Introduction to the Mathematics of Deep Learning
Buch von James V Stone
Sprache: Englisch

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Beschreibung
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.
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.
Über den Autor
Dr James Stone is an Honorary Reader in Vision and Computational Neuroscience at the University of Sheffield, England.
Details
Erscheinungsjahr: 2019
Genre: Informatik
Rubrik: Naturwissenschaften & Technik
Medium: Buch
Seiten: 218
ISBN-13: 9780956372826
ISBN-10: 0956372821
Sprache: Englisch
Ausstattung / Beilage: HC gerader Rücken kaschiert
Einband: Gebunden
Autor: Stone, James V
Hersteller: Sebtel Press
Maße: 235 x 157 x 16 mm
Von/Mit: James V Stone
Erscheinungsdatum: 01.04.2019
Gewicht: 0,475 kg
preigu-id: 120644276
Über den Autor
Dr James Stone is an Honorary Reader in Vision and Computational Neuroscience at the University of Sheffield, England.
Details
Erscheinungsjahr: 2019
Genre: Informatik
Rubrik: Naturwissenschaften & Technik
Medium: Buch
Seiten: 218
ISBN-13: 9780956372826
ISBN-10: 0956372821
Sprache: Englisch
Ausstattung / Beilage: HC gerader Rücken kaschiert
Einband: Gebunden
Autor: Stone, James V
Hersteller: Sebtel Press
Maße: 235 x 157 x 16 mm
Von/Mit: James V Stone
Erscheinungsdatum: 01.04.2019
Gewicht: 0,475 kg
preigu-id: 120644276
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