Übersicht			Auf mobile öffnen

Lieferstatus:	Auf Bestellung (Lieferzeit unbekannt)
Veröffentlichung:	Oktober 2018
Genre:	EDV / Informatik
	Android / Artificial Intelligence / B / Cloud / Code / Compiler und Übersetzer / Compilers & interpreters / Compilers and Interpreters / Java / Java (Computer program language) / Künstliche Intelligenz / mobile / Professional and Applied Computing / Programming / Programming & scripting languages# general / Programming Language / Programming languages (Electronic computers) / Programming Languages, Compilers, Interpreters / Supervised Learning / Unsupervised Learning mehr...
ISBN:	9781484239506
EAN-Code:	9781484239506
Verlag:	Springer EN
Einband:	Kartoniert
Sprache:	English
Dimensionen:	H 254 mm / B 178 mm / D
Gewicht:	787 gr
Seiten:	392
Illustration:	XXIII, 392 p. 155 illus., schwarz-weiss Illustrationen
Zus. Info:	EUDR exemption - product or manufacturing materials placed on the market prior to 31.12.2025.
Bewertung:	Titel bewerten / Meinung schreiben

Inhalt:

Build machine learning (ML) solutions for Java development. This book shows you that when designing ML apps, data is the key driver and must be considered throughout all phases of the project life cycle. Practical Java Machine Learning helps you understand the importance of data and how to organize it for use within your ML project. You will be introduced to tools which can help you identify and manage your data including JSON, visualization, NoSQL databases, and cloud platforms including Google Cloud Platform and Amazon Web Services. Practical Java Machine Learning includes multiple projects, with particular focus on the Android mobile platform and features such as sensors, camera, and connectivity, each of which produce data that can power unique machine learning solutions. You will learn to build a variety of applications that demonstrate the capabilities of the Google Cloud Platform machine learning API, including data visualizationfor Java; document classification using the Weka ML environment; audio file classification for Android using ML with spectrogram voice data; and machine learning using device sensor data. After reading this book, you will come away with case study examples and projects that you can take away as templates for re-use and exploration for your own machine learning programming projects with Java. What You Will Learn Identify, organize, and architect the data required for ML projects Deploy ML solutions in conjunction with cloud providers such as Google and Amazon Determine which algorithm is the most appropriate for a specific ML problem Implement Java ML solutions on Android mobile devices Create Java ML solutions to work with sensor data Build Java streaming based solutions Who This Book Is For Experienced Java developers who have not implemented machine learning techniques before.