TensorFlow智慧型移動項目（影印版）

《TensorFlow智慧型移動項目（影印版）》是2019年東南大學出版社出版的圖書，作者是[美] 傑夫·唐。

基本介紹

• 書名：TensorFlow智慧型移動項目（影印版）
• 作者：[美] 傑夫·唐
• 出版社：東南大學出版社
• 出版時間：2019年3月1日
• ISBN：9787564182908

內容簡介

　　作為一名開發人員，您總是需要留心並做好準備以應對即將發生的事情，同時還要尋堡陵關注當前趨勢。那么，有什麼比學習現在和未來這雅汽兩個世界的完美結合更好呢？人工智慧（AI）被廣泛認為是繼移動之後的下一個大產業，而谷歌的TensorFlow是領先的開源機器學習框架，也是人工智慧熱門的分支。這《TensorFlow智慧型移動項目（影印版 英文版）》涵蓋了10多個完整的以TensorFlow為引擎、運行各種很酷的TensorFlow模樂您蒸格潤婚型離線設備從頭開始構建的IOS、Android和樹莓派apps：從計算機視覺驗騙少、語音和語言處理到生成對抗網路和AlphaZero之類的深度學習。您將學習如何使用或重新訓練現有的TensorFlow模型，構建自己的模型，以及開發運行這些TensorFlow模型的智慧型堡采埋少移動apps。您將了解如何使用循序漸進的教程快速構建這樣的拔歸鍵茅app，以及如何利用大量來之不易的故障排除技巧來避免開發過程中的許多陷阱

目錄

Preface

Chapter 1： Getting Started with Mobile TensorFIow

Setting up TensorFIow

Setting up TensorFIow on MacOS

Setting up TensorFIow on GPU-powered Ubuntu

Setting up Xcode

Setting up Android Studio

TensorFIow Mobile vs TensorFIow Lite

Running sample TensorFIow iOS apps

Running sample TensorFIow Android apps

Summary

Chapter 2： Classifying Images with Transfer Learning

Transfer learning - what and why

Retraining using the Inception v3 model

Retraining using MobileNet models

Using the retrained models in the sample iOS app

Using the retrained models in the sample Android app

Adding TensorFIow to your own iOS app

Adding TensorFIow to your Objective-C iOS app

Adding TensorFIow to your Swift iOS app

Adding TensorFIow to your own Android app

Summary

Chapter 3： Detecting Objects and Their Locations

Object detection-a quick overview

Setting up the TensorFIow Object Detection API

Quick installation and example

Using pre-trained models

Retraining SSD-MobileNet and Faster RCNN models

Using object detection models in iOS

Building TensorFIow iOS libraries manually

Using TensorFIow iOS libraries in an app

Adding an object detection feature to an lOS app

Using YOLO2-another object-detection model

Summary

Chapter 4： Transforming Pictures with Amazing Art Styles

Neural Style Transfer - a quick overview

Training fast neural-style transfer models

Using fast neural-style transfer models in lOS

Adding and testing with fast neural transfer models

Looking back at the lOS code using fast neural transfer models

Using fast neural-style transfer models in Android

Using the TensorFIow Magenta multi-style model in lOS

Using the TensorFIow Magenta multi-style model in Android

Summary

Chapter 5： Understanding Simple Speech Commands

Speech recognition - a quick overview

Training a simple commands recognition model

Using a simple speech recognition model in Android

Building a new app using the model

Showing model-powered recognition results

Using a simple speech recognition model in lOS with Objective-C

Building a new app using the model

Fixing model-loading errors with tf_op_files．txt

Using a simple speech recognition model in lOS with Swift

Summary

Chapter 6： Describing Images in Natural Language

Image captioning - how it works

Training and freezing an image captioning model

Training and testing caption generation

Freezing the image captioning model

Transforming and optimizing the image captioning model

Fixing errors with transformed models

Optimizing the transformed model

Using the image captioning model in lOS

Using the image captioning model in Android

Summary

Chapter 7： Recognizing Drawing with CNN and LSTM

Drawing classification - how it works

Training， predicting， and preparing the drawing classification model

Training the drawing classification model

Predicting with the drawing classification model

Preparing the drawing classification model

Using the drawing classification model in lOS

Building custom TensorFIow library for lOS

Developing an lOS app to use the model

Using the drawing classification model in Android

Building custom TensorFIow library for Android

Developing an Android app to use the model

Summary

Chapter 8： Predicting Stock Price with RNN

RNN and stock price prediction - what and how

Using the TensorFIow RNN API for stock price prediction

Training an RNN model in TensorFIow

Testing the TensorFIow RNN model

Using the Keras RNN LSTM API for stock price prediction

Training an RNN model in Keras

Testing the Keras RNN model

Running the TensorFIow and Keras models on iOS

Running the TensorFIow and Keras models on Android

Summary

Chapter 9： Generating and Enhancing Images with GAN

GAN - what and why

Building and training GAN models with TensorFIow

Basic GAN model of generating handwritten digits

Advanced GAN model of enhancing image resolution

Using the GAN models in iOS

Using the basic GAN model

Using the advanced GAN model

Using the GAN models in Android

Using the basic GAN model

Using the advanced GAN model

Summary

Chapter 10： Building an AlphaZero-like Mobile Game App

AlphaZero - how does it work?

Training and testing an AlphaZero-like model for Connect 4

Training the model

Testing the model

Looking into the model-building code

Freezing the model

Using the model in iOS to play Connect 4

Using the model in Android to play Connect 4

Summary

Chapter 11： Using TensorFIow Lite and Core ML on Mobile

TensorFIow Lite - an overview

Using TensorFIow Lite in iOS

Running the example TensorFIow Lite iOS apps

Using a prebuilt TensorFIow Lite model in iOS

Using a retrained TensorFIow model for TensorFIow Lite in iOS

Using a custom TensorFIow Lite model in iOS

Using TensorFIow Lite in Android

Core ML for iOS - an overview

Using Core ML with Scikit-Learn machine learning

Building and converting the Scikit Learn models

Using the converted Core ML models in iOS

Using Core ML with Keras and TensorFIow

Summary

Chapter 12： Developing TensorFIow Apps on Raspberry Pi

Setting up Raspberry Pi and making it move

Setting up Raspberry Pi

Making Raspberry Pi move

Setting up TensorFIow on Raspberry Pi

Image recognition and text to speech

Audio recognition and robot movement

Reinforcement learning on Raspberry Pi

Understanding the CartPole simulated environment

Starting with basic intuitive policy

Using neural networks to build a better policy

Summary

Final words

Other Books You May Enjoy

Index

Adding TensorFIow to your own Android app

Summary

Chapter 3： Detecting Objects and Their Locations

Object detection-a quick overview

Setting up the TensorFIow Object Detection API

Quick installation and example

Using pre-trained models

Retraining SSD-MobileNet and Faster RCNN models

Using object detection models in iOS

Building TensorFIow iOS libraries manually

Using TensorFIow iOS libraries in an app

Adding an object detection feature to an lOS app

Using YOLO2-another object-detection model

Summary

Chapter 4： Transforming Pictures with Amazing Art Styles

Neural Style Transfer - a quick overview

Training fast neural-style transfer models

Using fast neural-style transfer models in lOS

Adding and testing with fast neural transfer models

Looking back at the lOS code using fast neural transfer models

Using fast neural-style transfer models in Android

Using the TensorFIow Magenta multi-style model in lOS

Using the TensorFIow Magenta multi-style model in Android

Summary

Chapter 5： Understanding Simple Speech Commands

Speech recognition - a quick overview

Training a simple commands recognition model

Using a simple speech recognition model in Android

Building a new app using the model

Showing model-powered recognition results

Using a simple speech recognition model in lOS with Objective-C

Building a new app using the model

Fixing model-loading errors with tf_op_files．txt

Using a simple speech recognition model in lOS with Swift

Summary

Chapter 6： Describing Images in Natural Language

Image captioning - how it works

Training and freezing an image captioning model

Training and testing caption generation

Freezing the image captioning model

Transforming and optimizing the image captioning model

Fixing errors with transformed models

Optimizing the transformed model

Using the image captioning model in lOS

Using the image captioning model in Android

Summary

Chapter 7： Recognizing Drawing with CNN and LSTM

Drawing classification - how it works

Training， predicting， and preparing the drawing classification model

Training the drawing classification model

Predicting with the drawing classification model

Preparing the drawing classification model

Using the drawing classification model in lOS

Building custom TensorFIow library for lOS

Developing an lOS app to use the model

Using the drawing classification model in Android

Building custom TensorFIow library for Android

Developing an Android app to use the model

Summary

Chapter 8： Predicting Stock Price with RNN

RNN and stock price prediction - what and how

Using the TensorFIow RNN API for stock price prediction

Training an RNN model in TensorFIow

Testing the TensorFIow RNN model

Using the Keras RNN LSTM API for stock price prediction

Training an RNN model in Keras

Testing the Keras RNN model

Running the TensorFIow and Keras models on iOS

Running the TensorFIow and Keras models on Android

Summary

Chapter 9： Generating and Enhancing Images with GAN

GAN - what and why

Building and training GAN models with TensorFIow

Basic GAN model of generating handwritten digits

Advanced GAN model of enhancing image resolution

Using the GAN models in iOS

Using the basic GAN model

Using the advanced GAN model

Using the GAN models in Android

Using the basic GAN model

Using the advanced GAN model

Summary

Chapter 10： Building an AlphaZero-like Mobile Game App

AlphaZero - how does it work?

Training and testing an AlphaZero-like model for Connect 4

Training the model

Testing the model

Looking into the model-building code

Freezing the model

Using the model in iOS to play Connect 4

Using the model in Android to play Connect 4

Summary

Chapter 11： Using TensorFIow Lite and Core ML on Mobile

TensorFIow Lite - an overview

Using TensorFIow Lite in iOS

Running the example TensorFIow Lite iOS apps

Using a prebuilt TensorFIow Lite model in iOS

Using a retrained TensorFIow model for TensorFIow Lite in iOS

Using a custom TensorFIow Lite model in iOS

Using TensorFIow Lite in Android

Core ML for iOS - an overview

Using Core ML with Scikit-Learn machine learning

Building and converting the Scikit Learn models

Using the converted Core ML models in iOS

Using Core ML with Keras and TensorFIow

Summary

Chapter 12： Developing TensorFIow Apps on Raspberry Pi

Setting up Raspberry Pi and making it move

Setting up Raspberry Pi

Making Raspberry Pi move

Setting up TensorFIow on Raspberry Pi

Image recognition and text to speech

Audio recognition and robot movement

Reinforcement learning on Raspberry Pi

Understanding the CartPole simulated environment

Starting with basic intuitive policy

Using neural networks to build a better policy

Summary

Final words

Other Books You May Enjoy

Index