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Machine learning for iOS developers

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Machine learning for iOS developers/ Abhishek Mishra.
作者:	Mishra, Abhishek.
出版者:	Hoboken, NJ :John Wiley & Sons, : c2020.,
面頁冊數:	1 online resource (xxxi, 327 p.) :ill. :
附註:	Includes index.
標題:	Computers. -
電子資源:	https://onlinelibrary.wiley.com/doi/book/10.1002/9781119602927
ISBN:	9781119602927

Machine learning for iOS developers
Mishra, Abhishek.

Machine learning for iOS developers[electronic resource] /Abhishek Mishra. - 1st ed. - Hoboken, NJ :John Wiley & Sons,c2020. - 1 online resource (xxxi, 327 p.) :ill.

Includes index.

Cover -- Title Page -- Copyright -- About the Author -- About the Technical Editor -- Acknowledgments -- Contents at a Glance -- Contents -- Introduction -- What Does This Book Cover? -- Additional Resources -- Reader Support for This Book -- Part 1 Fundamentals of Machine Learning -- Chapter 1 Introduction to Machine Learning -- What Is Machine Learning? -- Tools Commonly Used by Data Scientists -- Common Terminology -- Real-World Applications of Machine Learning -- Types of Machine Learning Systems -- Supervised Learning -- Unsupervised Learning -- Semisupervised Learning.

"Machine earning (ML) is the science of getting computers to act without being explicitly programmed. A branch of Artificial Intelligence (AI), machine learning techniques offer ways to identify trends, forecast behavior, and make recommendations. The Apple iOS Software Development Kit (SDK) allows developers to integrate ML services, such as speech recognition and language translation, into mobile devices, most of which can be used in multi-cloud settings. Focusing on Apple's ML services, Machine Learning for iOS Developers is an up-to-date introduction to the field, instructing readers to implement machine learning in iOS applications. Assuming no prior experience with machine learning, this reader-friendly guide offers expert instruction and practical examples of ML integration in iOS. Organized into two sections, the book's clearly-written chapters first cover fundamental ML concepts, the different types of ML systems, their practical uses, and the potential challenges of ML solutions. The second section teaches readers to use models--both pre-trained and user-built--with Apple's CoreML framework. Source code examples are provided for readers to download and use in their own projects. This book helps readers: Understand the theoretical concepts and practical applications of machine learning used in predictive data analytics Build, deploy, and maintain ML systems for tasks such as model validation, optimization, scalability, and real-time streaming Develop skills in data acquisition and modeling, classification, and regression. Compare traditional vs. ML approaches, and machine learning on handsets vs. machine learning as a service (MLaaS) Implement decision tree based models, an instance-based machine learning system, and integrate Scikit-learn & Keras models with CoreML Machine Learning for iOS Developers is a must-have resource software engineers and mobile solutions architects wishing to learn ML concepts and implement machine learning on iOS Apps." --Amazon.com.

ISBN: 9781119602927Subjects--Uniform Titles:

iOS (Electronic resource)
Subjects--Topical Terms:

565115
Computers.

LC Class. No.: Q325.5 / .M57 2020

Dewey Class. No.: 006.31

Machine learning for iOS developers
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500 $a Includes index.
505 0 $a Cover -- Title Page -- Copyright -- About the Author -- About the Technical Editor -- Acknowledgments -- Contents at a Glance -- Contents -- Introduction -- What Does This Book Cover? -- Additional Resources -- Reader Support for This Book -- Part 1 Fundamentals of Machine Learning -- Chapter 1 Introduction to Machine Learning -- What Is Machine Learning? -- Tools Commonly Used by Data Scientists -- Common Terminology -- Real-World Applications of Machine Learning -- Types of Machine Learning Systems -- Supervised Learning -- Unsupervised Learning -- Semisupervised Learning.
520 $a "Machine earning (ML) is the science of getting computers to act without being explicitly programmed. A branch of Artificial Intelligence (AI), machine learning techniques offer ways to identify trends, forecast behavior, and make recommendations. The Apple iOS Software Development Kit (SDK) allows developers to integrate ML services, such as speech recognition and language translation, into mobile devices, most of which can be used in multi-cloud settings. Focusing on Apple's ML services, Machine Learning for iOS Developers is an up-to-date introduction to the field, instructing readers to implement machine learning in iOS applications. Assuming no prior experience with machine learning, this reader-friendly guide offers expert instruction and practical examples of ML integration in iOS. Organized into two sections, the book's clearly-written chapters first cover fundamental ML concepts, the different types of ML systems, their practical uses, and the potential challenges of ML solutions. The second section teaches readers to use models--both pre-trained and user-built--with Apple's CoreML framework. Source code examples are provided for readers to download and use in their own projects. This book helps readers: Understand the theoretical concepts and practical applications of machine learning used in predictive data analytics Build, deploy, and maintain ML systems for tasks such as model validation, optimization, scalability, and real-time streaming Develop skills in data acquisition and modeling, classification, and regression. Compare traditional vs. ML approaches, and machine learning on handsets vs. machine learning as a service (MLaaS) Implement decision tree based models, an instance-based machine learning system, and integrate Scikit-learn & Keras models with CoreML Machine Learning for iOS Developers is a must-have resource software engineers and mobile solutions architects wishing to learn ML concepts and implement machine learning on iOS Apps." --Amazon.com.
588 $a Description based on print version record.
630 0 0 $a iOS (Electronic resource) $3 796786
650 0 $a Computers. $3 565115
650 0 $a Machine learning. $3 561253
856 4 0 $u https://onlinelibrary.wiley.com/doi/book/10.1002/9781119602927