作為一名開發(fā)人員，您總是需要留心并做好準備以應對即將發(fā)生的事情，同時還要關注當前趨勢。那么，有什么比學習現(xiàn)在和未來這兩個世界的完美結合更好呢？人工智能（AI）被廣泛認為是繼移動之后的下一個大產業(yè)，而谷歌的TensorFlow是*的開源機器學習框架，也是人工智能熱門的分支。這《TensorFlow智能移動項目（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、語音和語言處理到生成對抗網(wǎng)絡和AlphaZero之類的深度學習。您將學習如何使用或重新訓練現(xiàn)有的TensorFlow模型，構建自己的模型，以及開發(fā)運行這些TensorFlow模型的智能移動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(fā)過程中的許多陷阱
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
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Index
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