Deep Learning : Theory Architectures and Applications in Speech Image and Language Processing / edited by Gyanendra Verma and Rajesh Doriya.

Format:

Book

Author/Creator:

Verma, Gyanendra, Author.

Contributor:

Verma, Gyanendra, editor.

Doriya, Rajesh, editor.

Language:

English

Subjects (All):

Data mining.

Machine learning.

Physical Description:

1 online resource (270 pages)

Edition:

First edition.

Place of Publication:

Singapore : Bentham Science Publishers Pte. Ltd., 2023.

Summary:

This book is a detailed reference guide on deep learning and its applications. It aims to provide a basic understanding of deep learning and its different architectures that are applied to process images, speech, and natural language. It explains basic concepts and many modern use cases through fifteen chapters contributed by computer science academics and researchers. By the end of the book, the reader will become familiar with different deep learning approaches and models, and understand how to implement various deep learning algorithms using multiple frameworks and libraries. This book is divided into three parts. The first part explains the basic operating understanding, history, evolution, and challenges associated with deep learning. The basic concepts of mathematics and the hardware requirements for deep learning implementation, and some of its popular frameworks for medical applications are also covered. The second part is dedicated to sentiment analysis using deep learning and machine learning techniques. This book section covers the experimentation and application of deep learning techniques and architectures in real-world applications. It details the salient approaches, issues, and challenges in building ethically aligned machines. An approach inspired by traditional Eastern thought and wisdom is also presented. The final part covers artificial intelligence approaches used to explain the machine learning models that enhance transparency for the benefit of users. A review and detailed description of the use of knowledge graphs in generating explanations for black-box recommender systems and a review of ethical system design and a model for sustainable education is included in this section. An additional chapter demonstrates how a semi-supervised machine learning technique can be used for cryptocurrency portfolio management. The book is

a timely reference for academicians, professionals, researchers and students at engineering and medical institutions working on artificial intelligence applications.

Contents:

Cover

Title

Copyright

End User License Agreement

Contents

Foreword

Preface

List of Contributors

Deep Learning: History and Evolution

Application of Artificial Intelligence in Medical Imaging

Sampurna Panda1, Rakesh Kumar Dhaka1 and Babita Panda2,*

INTRODUCTION

MACHINE-LEARNING

Supervised Learning

Unsupervised Learning

Semi-supervised Learning

Active Learning

Reinforcement Learning

Evolutionary Learning

Introduction to Deep Learning

APPLICATION OF ML IN MEDICAL IMAGING

DEEP LEARNING IN MEDICAL IMAGING

Image Classification

Object Classification

Organ or Region Detection

Data Mining

The Sign-up Process

Other Imaging Applications

CONCLUSION

CONSENT FOR PUBLICATION Generated by AI.

Notes:

Includes bibliographical references.

Description based on publisher supplied metadata and other sources.

Part of the metadata in this record was created by AI, based on the text of the resource.

Description based on print version record.

ISBN:

9789815079210

9815079212

OCLC:

1399170098