Deep Learning

A Practical Introduction

Manel Martinez-Ramon,Meenu Ajith,Aswathy Rajendra Kurup

• Publisher: Wiley
• Year: 2024
• ISBN:
  9781119861874
• Edition: 1
• Topic: Ciencia de la computación
• Subtopic: Redes neuronales

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.

Table of contents

• Cover
• Title Page
• Copyright
• Contents
• About the Authors
• Foreword
• Preface
• Acknowledgment
• About the Companion Website
• Chapter 1 The Multilayer Perceptron
• Chapter 2 Training Practicalities
• Chapter 3 Deep Learning Tools
• Chapter 4 Convolutional Neural Networks
• Chapter 5 Recurrent Neural Networks
• Chapter 6 Attention Networks and Transformers
• Chapter 7 Deep Unsupervised Learning I
• Chapter 8 Deep Unsupervised Learning II
• Chapter 9 Deep Bayesian Networks
• List of Acronyms
• Notation
• Bibliography
• Index
• EULA