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

Mark Wickham

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

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商品描述

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.

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.

商品描述(中文翻譯)

建構機器學習（ML）解決方案的Java開發者，這本書向您展示了在設計ML應用程式時，資料是關鍵驅動因素，必須在項目生命週期的所有階段中予以考慮。《實用Java機器學習》幫助您了解資料的重要性，以及如何組織資料以供ML項目使用。您將介紹一些工具，這些工具可以幫助您識別和管理資料，包括JSON、可視化、NoSQL資料庫以及Google Cloud Platform和Amazon Web Services等雲平台。

《實用Java機器學習》包含多個項目，特別關注Android移動平台和傳感器、相機和連接性等功能，這些功能產生的資料可以驅動獨特的機器學習解決方案。您將學習構建各種應用程式，展示Google Cloud Platform機器學習API的能力，包括Java的資料可視化；使用Weka ML環境進行文件分類；使用ML和聲譜圖語音資料進行Android的音訊檔案分類；以及使用設備傳感器資料進行機器學習。

閱讀本書後，您將獲得案例研究示例和項目，可作為模板供您在自己的Java機器學習編程項目中重複使用和探索。

您將學到什麼：
- 識別、組織和架構ML項目所需的資料
- 與Google和Amazon等雲服務提供商一起部署ML解決方案
- 確定哪種演算法最適合特定的ML問題
- 在Android移動設備上實現Java ML解決方案
- 創建與傳感器資料一起工作的Java ML解決方案
- 構建基於Java串流的解決方案

本書適合對Java有經驗但尚未實施機器學習技術的開發者。

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