Deep Leaing:A Practical Introduction

by:Manel Martinez-Ramon (Author),Meenu Ajith(Author),Aswathy Rajendra Kurup(Author)&0more

Publisher:Wiley

Edition:1st

Publication Date:2024/7/15

Language:English

Print Length:416 pages

ISBN-10:1119861861

ISBN-13:9781119861867

Book Description

An engaging and accessible introduction to deep leaing perfect for students and professionalsIn Deep Leaing:A Practical Introduction, a team of distinguished researchers delivers a book complete with coverage of the theoretical and practical elements of deep leaing. The book includes extensive examples, end-of-chapter exercises, homework, exam material, and a GitHub repository containing code and data for all provided examples. Combining contemporary deep leaing theory with state-of-the-art tools, the chapters are structured to maximize accessibility for both beginning and intermediate students. The authors have included coverage of TensorFlow, Keras, and Pytorch. Readers will also find:Thorough introductions to deep leaing and deep leaing toolsComprehensive explorations of convolutional neural networks, including discussions of their elements, operation, training, and architecturesPractical discussions of recurrent neural networks and non-supervised approaches to deep leaingFulsome treatments of generative adversarial networks as well as deep Bayesian neural networksPerfect for undergraduate and graduate students studying computer vision, computer science, artificial intelligence, and neural networks, Deep Leaing:A Practical Introduction will also benefit practitioners and researchers in the fields of deep leaing and machine leaing in general.

About the Author

An engaging and accessible introduction to deep leaing perfect for students and professionalsIn Deep Leaing:A Practical Introduction, a team of distinguished researchers delivers a book complete with coverage of the theoretical and practical elements of deep leaing. The book includes extensive examples, end-of-chapter exercises, homework, exam material, and a GitHub repository containing code and data for all provided examples. Combining contemporary deep leaing theory with state-of-the-art tools, the chapters are structured to maximize accessibility for both beginning and intermediate students. The authors have included coverage of TensorFlow, Keras, and Pytorch. Readers will also find:Thorough introductions to deep leaing and deep leaing toolsComprehensive explorations of convolutional neural networks, including discussions of their elements, operation, training, and architecturesPractical discussions of recurrent neural networks and non-supervised approaches to deep leaingFulsome treatments of generative adversarial networks as well as deep Bayesian neural networksPerfect for undergraduate and graduate students studying computer vision, computer science, artificial intelligence, and neural networks, Deep Leaing:A Practical Introduction will also benefit practitioners and researchers in the fields of deep leaing and machine leaing in general.

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