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Deep Learning

A Practical Introduction

By: Manel Martinez-Ramon,Meenu Ajith,Aswathy Rajendra Kurup

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Deep Learning

A Practical Introduction

By: Manel Martinez-Ramon,Meenu Ajith,Aswathy Rajendra Kurup

An engaging and accessible introduction to deep learning perfect for students and professionals In Deep Learning: A Practical Introduction, a team of distinguished researchers delivers a book complete with coverage of the theoretical and practical elements of deep learning. 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 learning 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 learning and deep learning tools Comprehensive explorations of convolutional neural networks, including discussions of their elements, operation, training, and architectures Practical discussions of recurrent neural networks and non-supervised approaches to deep learning Fulsome treatments of generative adversarial networks as well as deep Bayesian neural networks Perfect for undergraduate and graduate students studying computer vision, computer science, artificial intelligence, and neural networks, Deep Learning: A Practical Introduction will also benefit practitioners and researchers in the fields of deep learning and machine learning in general.

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Page Count

421

Publisher

John Wiley & Sons

Published Date

ISBN 10

1119861888

ISBN 13

9781119861881

Deep Learning

A Practical Introduction

By: Manel Martinez-Ramon,Meenu Ajith,Aswathy Rajendra Kurup

Write a review Read reviews Take a Quiz Solve Book Puzzle

Deep Learning

A Practical Introduction

By: Manel Martinez-Ramon,Meenu Ajith,Aswathy Rajendra Kurup

An engaging and accessible introduction to deep learning perfect for students and professionals In Deep Learning: A Practical Introduction, a team of distinguished researchers delivers a book complete with coverage of the theoretical and practical elements of deep learning. 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 learning 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 learning and deep learning tools Comprehensive explorations of convolutional neural networks, including discussions of their elements, operation, training, and architectures Practical discussions of recurrent neural networks and non-supervised approaches to deep learning Fulsome treatments of generative adversarial networks as well as deep Bayesian neural networks Perfect for undergraduate and graduate students studying computer vision, computer science, artificial intelligence, and neural networks, Deep Learning: A Practical Introduction will also benefit practitioners and researchers in the fields of deep learning and machine learning in general.

Average ratings

N/A

5

0%

4

0%

3

0%

2

0%

1

0%

Solve jigsaw puzzle

Age

Page Count

421

Publisher

John Wiley & Sons

Published Date

ISBN 10

1119861888

ISBN 13

9781119861881