Fundamentals and Methods of Machine and Deep Learning. Pradeep Singh
Чтение книги онлайн.
Читать онлайн книгу Fundamentals and Methods of Machine and Deep Learning - Pradeep Singh страница 10
Название: Fundamentals and Methods of Machine and Deep Learning
Автор: Pradeep Singh
Издательство: John Wiley & Sons Limited
Жанр: Программы
isbn: 9781119821885
isbn:
© 2022 Scrivener Publishing LLC
For more information about Scrivener publications please visit www.scrivenerpublishing.com.
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.
Wiley Global Headquarters 111 River Street, Hoboken, NJ 07030, USA
For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com.
Limit of Liability/Disclaimer of Warranty While the publisher and authors have used their best efforts in preparing this work, they make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives, written sales materials, or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further information does not mean that the publisher and authors endorse the information or services the organization, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read.
Library of Congress Cataloging-in-Publication Data
ISBN 978-1-119-82125-0
Cover image: Pixabay.Com
Cover design by Russell Richardson
Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines
Printed in the USA
10 9 8 7 6 5 4 3 2 1
Preface
Over the past two decades, the field of machine learning and its subfield deep learning have played a main role in software applications development. Also, in recent research studies, they are regarded as one of the disruptive technologies that will transform our future life, business and the global economy. The recent explosion of digital data in a wide variety of domains, including science, engineering, internet of things, biomedical, healthcare and many business sectors, has declared the era of big data, which cannot be analyzed by classical statistics but by the more modern, robust machine learning and deep learning techniques. Since machine learning learns from data rather than by programming hard-coded decision rules, an attempt is being made to use machine learning to make computers that are able to solve problems like human experts in the field.
The goal of this book is to present a practical approach by explaining the concepts of machine learning and deep learning algorithms with applications. Supervised machine learning algorithms, ensemble machine learning algorithms, feature selection, deep learning techniques, and their applications are discussed. Also included in the eighteen chapters is unique information which provides a clear understanding of concepts by using algorithms and case studies illustrated with applications of machine learning and deep learning in different domains, including disease prediction, software defect prediction, online television analysis, medical image processing, etc. Each of the chapters briefly described below provides both a chosen approach and its implementation.
Chapter 1 assists in learning supervised machine learning algorithms and their applications.
Chapter 2 discusses the detection of zonotic diseases using ensemble machine learning algorithms.
Chapter 3 provides machine learning model evaluation techniques.
Chapter 4 analyzes MSEIR and LSTM models for the prediction of COVID-19 using RMSLE.
Chapter 5 discusses the significance of feature selection techniques in machine learning.
Chapter 6 provides insight into the development of disease prediction systems using machine learning and deep learning.
Chapter 7 discusses the detection of diabetic retinopathy using ensemble learning techniques.
Chapter 8 presents a case study for medical analysis of heart disease using machine learning and deep learning.
Chapter 9 discusses a novel convolutional neural network model to predict software defects.
Chapter 10 familiarizes the reader with the process of predictive analysis on online television videos using machine learning algorithms.
Chapter 11 discusses a combinational deep learning approach to visually evoked EEG-based image classification.
Chapter 12 gives a comparative analysis of machine learning algorithms with balancing techniques for credit card fraud detection.
Chapter 13 describes crack detection in civil structures using deep learning.
Chapter 14 discusses measuring urban sprawl using machine learning.
Chapter 15 is all about the applications of deep learning algorithms in medical image processing.
Chapter 16 assists in understanding the simulation of self-driving cars based on deep learning.
Chapter 17 discusses assistive technologies for visual СКАЧАТЬ