Deep Learning

A Comprehensive Guide

Shriram K Vasudevan, Sini Raj Pulari, Subashri Vasudevan

• Editorial: Chapman and Hall/CRC
• Año: 2021
• ISBN:
  9781000481884
• Edición: 1
• Categoría: Informatik
• Categoría: Informatik Allgemein

Deep Learning: A Comprehensive Guide provides comprehensive coverage of Deep Learning (DL) and Machine Learning (ML) concepts. DL and ML are the most sought-after domains, requiring a deep understanding – and this book gives no less than that. This book enables the reader to build innovative and useful applications based on ML and DL. Starting with the basics of neural networks, and continuing through the architecture of various types of CNNs, RNNs, LSTM, and more till the end of the book, each and every topic is given the utmost care and shaped professionally and comprehensively.

Key Features

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• Includes the smooth transition from ML concepts to DL concepts

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• Line-by-line explanations have been provided for all the coding-based examples

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• Includes a lot of real-time examples and interview questions that will prepare the reader to take up a job in ML/DL right away

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• Even a person with a non-computer-science background can benefit from this book by following the theory, examples, case studies, and code snippets

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• Every chapter starts with the objective and ends with a set of quiz questions to test the reader's understanding

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• Includes references to the related YouTube videos that provide additional guidance

AI is a domain for everyone. This book is targeted toward everyone irrespective of their field of specialization. Graduates and researchers in deep learning will find this book useful.

Índice

• Cover
• Half-Title Page
• Title Page
• Copyright Page
• Table of Contents
• Preface
• The Authors
• Chapter 1 Introduction to Deep Learning
• Chapter 2 The Tools and the Prerequisites
• Chapter 3 Machine Learning: The Fundamentals
• Chapter 4 The Deep Learning Framework
• Chapter 5 CNN – Convolutional Neural Networks: A Complete Understanding
• Chapter 6 CNN Architectures: An Evolution
• Chapter 7 Recurrent Neural Networks
• Chapter 8 Autoencoders
• Chapter 9 Generative Models
• Chapter 10 Transfer Learning
• Chapter 11 Intel OpenVino: A Must-Know Deep Learning Toolkit
• Chapter 12 Interview Questions and Answers
• Index