9783319730035-3319730037-Introduction to Deep Learning: From Logical Calculus to Artificial Intelligence (Undergraduate Topics in Computer Science)

Introduction to Deep Learning: From Logical Calculus to Artificial Intelligence (Undergraduate Topics in Computer Science)

ISBN-13: 9783319730035
ISBN-10: 3319730037
Edition: 1st ed. 2018
Author: Skansi, Sandro
Publication date: 2018
Publisher: Springer
Format: Paperback 204 pages
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Book details

ISBN-13: 9783319730035
ISBN-10: 3319730037
Edition: 1st ed. 2018
Author: Skansi, Sandro
Publication date: 2018
Publisher: Springer
Format: Paperback 204 pages

Summary

Acknowledged authors Skansi, Sandro wrote Introduction to Deep Learning: From Logical Calculus to Artificial Intelligence (Undergraduate Topics in Computer Science) comprising 204 pages back in 2018. Textbook and eTextbook are published under ISBN 3319730037 and 9783319730035. Since then Introduction to Deep Learning: From Logical Calculus to Artificial Intelligence (Undergraduate Topics in Computer Science) textbook was available to sell back to BooksRun online for the top buyback price of $ 2.00 or rent at the marketplace.

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, LSTMs, Word2vec, RBMs, DBNs, 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.

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