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

James V Stone

ISBN 10: 0956372821 / ISBN 13: 9780956372826

Edité par Sebtel Press, 2019

A propos de cet article

nach der Bestellung gedruckt Neuware - Printed after ordering - 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.

N° de réf. du vendeur 9780956372826

Détails bibliographiques

• Titre: Artificial Intelligence Engines : A Tutorial Introduction to the Mathematics of Deep Learning
• Éditeur: Sebtel Press
• Date de parution: 2019
• Langue: anglais
• ISBN à 10 chiffres: 0956372821
• ISBN à 13 chiffres: 9780956372826
• Reliure: Buch
• État de l'article: Neu
• Dimensions: 235 L x 157 H x 16 P (en millimètres) 235 millimètres de largeur par 157 millimètres de hauteur par 16 millimètres de profondeur
• Poids: 473 grammes

A propos de ce titre

Synopsis

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.