Deep Learning
eBook - PDF

Deep Learning

A Practical Introduction

  1. English
  2. PDF
  3. Available on iOS & Android
eBook - PDF

Deep Learning

A Practical Introduction

About this book

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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Yes, you can access Deep Learning by Manel Martinez-Ramon,Meenu Ajith,Aswathy Rajendra Kurup in PDF and/or ePUB format, as well as other popular books in Computer Science & Neural Networks. We have over one million books available in our catalogue for you to explore.

Information

Publisher
Wiley
Year
2024
Print ISBN
9781119861867
eBook ISBN
9781119861874

Table of contents

  1. Cover
  2. Title Page
  3. Copyright
  4. Contents
  5. About the Authors
  6. Foreword
  7. Preface
  8. Acknowledgment
  9. About the Companion Website
  10. Chapter 1 The Multilayer Perceptron
  11. Chapter 2 Training Practicalities
  12. Chapter 3 Deep Learning Tools
  13. Chapter 4 Convolutional Neural Networks
  14. Chapter 5 Recurrent Neural Networks
  15. Chapter 6 Attention Networks and Transformers
  16. Chapter 7 Deep Unsupervised Learning I
  17. Chapter 8 Deep Unsupervised Learning II
  18. Chapter 9 Deep Bayesian Networks
  19. List of Acronyms
  20. Notation
  21. Bibliography
  22. Index
  23. EULA