Machine Learning for Mobile: Empowering mobiles with the Artificial Intelligence capabilities using TensorFlow, Core ML and Caffe2Go
暫譯: 行動裝置的機器學習:利用 TensorFlow、Core ML 和 Caffe2Go 賦予行動裝置人工智慧能力

Revathi Gopalakrishnan, Avinash Venkateswarlu

相關主題

商品描述

Embark smartphones with smart and innovative mobile applications using the power of Machine Learning

Key Features

  • Practical understanding of machine learning concept and algorithms like clustering, classification, regression with thorough use-cases
  • End-to-end coverage of on-device implementation of mobile ML applications using popular Core ML and TensorFlow Lite libraries
  • Grasp all about machine learning and build smart mobile application across Android and iOS devices.

Book Description

Machine Learning, represents an ultimate new era in software development enabling computers, mobiles and other devices to complete critical tasks without any special programming, thus allowing smartphones to produce an enormous amount useful data that can be mined analyzed and used to make predictions in the field of machine learning. This book will help you with how to deal with machine learning on mobile with easy to follow practical examples.

The book begins with giving you an introduction to machine learning on mobile and provides useful insights to be comfortable with the subject. You will then dive deep into supervised and unsupervised learning on mobile. Within this section, the book would cover important machine learning tools for mobile devices such as clustering, classification, regression followed by popular algorithms - Naive Bayes and Logistic Regression. You will also get to learn how to build a machine learning model using mobile-based libraries such as CoreML, Caffe2Go, Tensorflow lite and Weka on Android and iOS platform using SDKs. Next, you get to understand machine learning on cloud and how cloud services for machine learning are used in mobiles. Finally, the book would also cover an experiment on performing on-device image classification using mobile-based Tensorflow Lite and caffe2Go framework helping you to get a thorough understanding in building an artificial intelligence engine that runs directly on mobile devices.

By the end of this book, you would get a thorough understanding of machine learning models, performing on-device machine learning thereby enabling you to run artificial intelligence in real-time on mobile devices.

What you will learn

  • Understand the tools such as CoreML, Caffe2Go, Tensorflow lite and Weka and libraries available to carry out mobile machine learning.
  • Demystify supervised learning with classification and regression on mobile.
  • Learn unsupervised learning on Android and iOS using k-means clustering and association algorithms.
  • Perform practical exercises of building image classification using tensorflow lite and caffe2go
  • Explore Cloud services for machine learning that can be used in mobile apps.

Who This Book Is For

The book is intended for mobile developers and machine learning users who are aspiring to take machine learning forward to mobiles and smart devices. Basic knowledge of machine learning and entry-level experience in mobile application development is preferred.

商品描述(中文翻譯)

**利用機器學習的力量,為智慧型手機開發智能創新的行動應用程式**

#### 主要特點
- 實用的機器學習概念和演算法理解,包括聚類、分類、回歸,並附有詳細的使用案例
- 使用流行的 Core ML 和 TensorFlow Lite 函式庫,全面涵蓋行動機器學習應用程式的裝置端實作
- 全面掌握機器學習,並在 Android 和 iOS 裝置上構建智能行動應用程式。

#### 書籍描述
機器學習代表了軟體開發的一個全新時代,使計算機、手機和其他裝置能夠在沒有特殊編程的情況下完成關鍵任務,從而使智慧型手機能夠產生大量有用的數據,這些數據可以被挖掘、分析並用於機器學習領域的預測。本書將幫助您了解如何在行動裝置上處理機器學習,並提供易於跟隨的實用範例。

本書首先介紹行動裝置上的機器學習,並提供有用的見解以便您能夠熟悉這個主題。接著,您將深入了解行動裝置上的監督式學習和非監督式學習。在這一部分中,本書將涵蓋行動裝置的重要機器學習工具,如聚類、分類、回歸,並介紹流行的演算法 - Naive Bayes 和邏輯回歸。您還將學習如何使用基於行動的函式庫(如 CoreML、Caffe2Go、TensorFlow Lite 和 Weka)在 Android 和 iOS 平台上構建機器學習模型。接下來,您將了解雲端機器學習以及雲端服務如何在行動裝置上使用。最後,本書還將涵蓋一個實驗,使用基於行動的 TensorFlow Lite 和 Caffe2Go 框架進行裝置端圖像分類,幫助您深入了解如何構建直接在行動裝置上運行的人工智慧引擎。

在本書結束時,您將全面了解機器學習模型,並能夠在裝置端執行機器學習,從而使您能夠在行動裝置上實時運行人工智慧。

#### 您將學到什麼
- 了解如 CoreML、Caffe2Go、TensorFlow Lite 和 Weka 等工具及可用於行動機器學習的函式庫。
- 解密行動裝置上的監督式學習,包括分類和回歸。
- 學習在 Android 和 iOS 上使用 k-means 聚類和關聯演算法進行非監督式學習。
- 實踐使用 TensorFlow Lite 和 Caffe2Go 構建圖像分類的實際練習。
- 探索可用於行動應用程式的機器學習雲端服務。

#### 本書適合誰
本書適合希望將機器學習推向行動裝置和智能設備的行動開發者和機器學習使用者。建議具備基本的機器學習知識和入門級的行動應用程式開發經驗。