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Practical Java Machine Learning = Projects with Google Cloud Platform and Amazon Web Services /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Practical Java Machine Learning/ by Mark Wickham.
其他題名:	Projects with Google Cloud Platform and Amazon Web Services /
作者:	Wickham, Mark.
面頁冊數:	XXIII, 392 p. 155 illus.online resource. :
Contained By:	Springer Nature eBook
標題:	Java (Computer program language). -
電子資源:	https://doi.org/10.1007/978-1-4842-3951-3
ISBN:	9781484239513

Practical Java Machine Learning = Projects with Google Cloud Platform and Amazon Web Services /
Wickham, Mark.

Practical Java Machine LearningProjects with Google Cloud Platform and Amazon Web Services /[electronic resource] :by Mark Wickham. - 1st ed. 2018. - XXIII, 392 p. 155 illus.online resource.

1. Introduction -- 2. Data: The Fuel for Machine Learning -- 3. Leveraging Cloud Platforms -- 4. Algorithms: The Brains of Machine Learning -- 5. Java Machine Learning Environments -- 6. Integrating Models.

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 visualization for 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. You will: 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.

ISBN: 9781484239513

Standard No.: 10.1007/978-1-4842-3951-3doiSubjects--Topical Terms:

686374
Java (Computer program language).

LC Class. No.: QA76.73.J38

Dewey Class. No.: 005.133

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