Skansi Sandro
Skansi Sandro - Introduction To Deep Learning From Logical Calculus To Artificial Intelligence - Paperback
Skansi Sandro - Introduction To Deep Learning From Logical Calculus To Artificial Intelligence - Paperback
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Binding: Paperback
Description: This textbook presents a concise accessible and engaging first introduction to deep learning offering a wide range of connectionist models which represent the current state - of - the - art. The text explores the most popular algorithms and architectures in a simple and intuitive style explaining the mathematical derivations in a step - by - step manner. The content coverage includes convolutional networks LST Ms Word 2vec RB Ms DB Ns neural Turing machines memory networks and autoencoders. Numerous examples in working Python code are provided throughout the book and the code is also supplied separately at an accompanying website. Topics and features: introduces the fundamentals of machine learning and the mathematical and computational prerequisites for deep learning; discusses feed - forward neural networks and explores the modifications to these which can be applied to any neural network; examines convolutional neural networks and the recurrent connections to a feed - forward neural network; describes the notion of distributed representations the concept of the autoencoder and the ideas behind language processing with deep learning; presents a brief history of artificial intelligence and neural networks and reviews interesting open research problems in deep learning and connectionism. This clearly written and lively primer on deep learning is essential reading for graduate and advanced undergraduate students of computer science cognitive science and mathematics as well as fields such as linguistics logic philosophy and psychology.
Title: Introduction To Deep Learning From Logical Calculus To Artificial Intelligence
Author(s): Skansi Sandro
Publisher: Springer International Publishing Ag
Barcode: 9783319730035
Pages: 191 Pages, 38 Illustrations, Black And White; Xiii, 191 P. 38 Illus.
Publication Date: 2/15/2018
Series: Undergraduate Topics in Computer Science
Category: Pattern Recognition
Description: This textbook presents a concise accessible and engaging first introduction to deep learning offering a wide range of connectionist models which represent the current state - of - the - art. The text explores the most popular algorithms and architectures in a simple and intuitive style explaining the mathematical derivations in a step - by - step manner. The content coverage includes convolutional networks LST Ms Word 2vec RB Ms DB Ns neural Turing machines memory networks and autoencoders. Numerous examples in working Python code are provided throughout the book and the code is also supplied separately at an accompanying website. Topics and features: introduces the fundamentals of machine learning and the mathematical and computational prerequisites for deep learning; discusses feed - forward neural networks and explores the modifications to these which can be applied to any neural network; examines convolutional neural networks and the recurrent connections to a feed - forward neural network; describes the notion of distributed representations the concept of the autoencoder and the ideas behind language processing with deep learning; presents a brief history of artificial intelligence and neural networks and reviews interesting open research problems in deep learning and connectionism. This clearly written and lively primer on deep learning is essential reading for graduate and advanced undergraduate students of computer science cognitive science and mathematics as well as fields such as linguistics logic philosophy and psychology.
Title: Introduction To Deep Learning From Logical Calculus To Artificial Intelligence
Author(s): Skansi Sandro
Publisher: Springer International Publishing Ag
Barcode: 9783319730035
Pages: 191 Pages, 38 Illustrations, Black And White; Xiii, 191 P. 38 Illus.
Publication Date: 2/15/2018
Series: Undergraduate Topics in Computer Science
Category: Pattern Recognition
