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Beginning Machine Learning in iOS - Mohit Thakkar

Beginning Machine Learning in iOS (eBook)

CoreML Framework

(Autor)

eBook Download: PDF
2019 | 1st ed.
XI, 157 Seiten
Apress (Verlag)
978-1-4842-4297-1 (ISBN)
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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. 
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 LearnUnderstand the CoreML componentsTrain custom modelsImplement GPU processing for better computation efficiencyEnable machine learning in your application Who This Book Is ForNovice 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. 

Beginning Machine Learning in iOSChapter 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 

Erscheint lt. Verlag 20.2.2019
Zusatzinfo XI, 157 p. 112 illus.
Verlagsort Berkeley
Sprache englisch
Themenwelt Informatik Betriebssysteme / Server iOS
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 1-4842-4297-1 / 1484242971
ISBN-13 978-1-4842-4297-1 / 9781484242971
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