机器与深度学习基础与方法:算法、工具与应用 Fundamentals And Methods Of Machine And Deep Learning: Algorithms, Tools, And Applications

基本信息

Edited by:Pradeep Singh

Format:Hardback 480 pages

Publisher:John Wiley & Sons Inc

Imprint:Wiley-Scrivener

ISBN:9781119821250

Published:25 Feb 2022

Weight:792g

Dimensions:160 x 240 x 30 (mm)

信息仅供参考，具体请以到手的实物为准

书籍简介●

机器学习和深度学习基础知识和方法

本书通过解释机器学习和深度学习算法的概念、方法论进展的评估以及算法的应用演示，提供了实用的方法。

在过去的二十年里，机器学习领域及其子领域深度学习在软件应用程序开发中发挥了主要作用。 此外，在研究中，它们被视为将改变我们未来的生活、商业和全球经济的颠覆性技术之一。 科学、工程、物联网、生物医学、医疗保健和许多商业领域等各个领域的数字数据爆炸式增长，宣告了大数据时代的到来，大数据不能用经典的统计数据来分析，而是可以用更广泛的方法来分析。 现代、强大的机器学习和深度学习技术。 由于机器学习是从数据中学习，而不是通过编写硬编码的决策规则来学习，因此人们正在尝试使用机器学习来制造能够像该领域的人类专家一样解决问题的计算机。

本书的目标是通过应用解释机器学习和深度学习算法的概念，提出实用的方法。 讨论了监督机器学习算法、集成机器学习算法、特征选择、深度学习技术及其应用。 十八章中还包含独特的信息，通过使用算法和案例研究来清晰地理解概念，并说明机器学习和深度学习在不同领域的应用，包括疾病预测、软件缺陷预测、在线电视分析、医学图像处理 下简要描述的每一章都提供了所选方法及其实现。

适用读者：

人工智能研究人员和工程师、计算机科学家以及软件开发人员。

FUNDAMENTALS AND METHODS OF MACHINE AND DEEP LEARNING

The book provides a practical approach by explaining the concepts of machine learning and deep learning algorithms, evaluation of methodology advances, and algorithm demonstrations with applications.

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 analysed 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.

Audience

Researchers and engineers in artificial intelligence, computer scientists as well as software developers.

作者简介

Pradeep Singh 博士，赖布尔国家理工学院计算机科学工程系助理教授。 他目前的研究兴趣包括机器学习、深度学习、进化计算、软件质量实证研究和软件故障预测模型。

Pradeep Singh PhD, is an assistant professor in the Department of Computer Science Engineering, National Institute of Technology Raipur. His current research interests include machine learning, deep learning, evolutionary computing, empirical studies on software quality, and software fault prediction models.

目录

部分目录，仅供参考

Preface xix

1 Supervised Machine Learning: Algorithms and Applications 1

Shruthi H. Shetty, Sumiksha Shetty, Chandra Singh and Ashwath Rao

1.1 History 2

1.2 Introduction 2

1.3 Supervised Learning 4

1.4 Linear Regression (LR) 5

1.4.1 Learning Model 6

1.4.2 Predictions With Linear Regression 7

1.5 Logistic Regression 8

1.6 Support Vector Machine (SVM) 9

1.7 Decision Tree 11

1.8 Machine Learning Applications in Daily Life 12

1.8.1 Traffic Alerts (Maps) 12

1.8.2 Social Media (Facebook) 13

1.8.3 Transportation and Commuting (Uber) 13

1.8.4 Products Recommendations 13

1.8.5 Virtual Personal Assistants 13

1.8.6 Self-Driving Cars 14

1.8.7 Google Translate 14

1.8.8 Online Video Streaming (Netflix) 14

1.8.9 Fraud Detection 14

1.9 Conclusion 15

References 15

2 Zonotic Diseases Detection Using Ensemble Machine Learning Algorithms 17