Beginning Machine Learning in iOS - Mohit Thakkar

Beginning Machine Learning in iOS

CoreML Framework

(Autor)

Buch | Softcover
157 Seiten
2019 | 1st ed.
Apress (Verlag)
978-1-4842-4296-4 (ISBN)
26,74 inkl. MwSt
Implement machine learning models in your iOS applications. This short work begins by reviewing the primary principals of machine learning and then moves on to discussing more advanced topics, such as CoreML, the framework used to enable machine learning tasks in Apple products. 
Many applications on iPhone use machine learning: Siri to serve voice-based requests, the Photos app for facial recognition, and Facebook to suggest which people that might be in a photo.  You'll review how these types of machine learning tasks are implemented and performed so that you can use them in your own apps. 
Beginning Machine Learning in iOS is your guide to putting machine learning to work in your iOS applications.
What You'll Learn

Understand the CoreML components

Train custom models

Implement GPU processing for better computation efficiency

Enable machine learning in your application 


Who This Book Is For
Novice developers and programmers who wish to implement machine learning in their iOS applications and those who want to learn the fundamentals about machine learning. 

Mohit Thakkar is an Associate Software Engineer with MNC. He has a bachelor's degree in computer engineering and is the author of several independently published titles, including Artificial Intelligence, Data Mining & Business Intelligence, iOS Programming, and Mobile Computing & Wireless Communication. He also published a research paper titled “Remote Health Monitoring using Implantable Probes to Prevent Untimely Death of Animals” in the International Journal of Advanced Research in Management, Architecture, Technology and Engineering. 

Chapter 1. Introduction to Machine Learning.- Chapter 2. Introduction to Core ML Framework.- Chapter 3. Custom ML Models Using Turi Create.-  Chapter 4. Custom Core ML Models using Create ML.- Chapter 5. Improving Computational Efficiency.   

Erscheinungsdatum
Zusatzinfo 112 Illustrations, black and white; XI, 157 p. 112 illus.
Verlagsort Berkley
Sprache englisch
Maße 155 x 235 mm
Themenwelt Informatik Betriebssysteme / Server iOS
Informatik Betriebssysteme / Server Macintosh / Mac OS X
Informatik Programmiersprachen / -werkzeuge Mac / Cocoa Programmierung
Informatik Theorie / Studium Künstliche Intelligenz / Robotik
Schlagworte Apple • Artificial Intelligence • Computer Science • CoreML • image classification • iOS development • machine learning • Mobile Development
ISBN-10 1-4842-4296-3 / 1484242963
ISBN-13 978-1-4842-4296-4 / 9781484242964
Zustand Neuware
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