TensorFlow智能移动项目(影印版) 东南大学出版社 阿里云 lit azw3 txt pdf caj 下载 在线

作为一名开发人员，您总是需要留心并做好准备以应对即将发生的事情，同时还要关注当前趋势。那么，有什么比学习现在和未来这两个世界的完美结合更好呢？人工智能（AI）被广泛认为是继移动之后的下一个大产业，而谷歌的TensorFlow是靠前的开源机器学习框架，也是人工智能很热门的分支。这本书涵盖了10多个完整的以TensorFlow为引擎、运行各种很酷的TensorFlow模型离线设备从头开始构建的IOS、Android和树莓派apps：从计算机视觉、语音和语言处理到生成对抗网络和AlphaZero之类的深度学习。您将学习如何使用或重新训练现有的TensorFlow模型，构建自己的模型，以及开发运行这些TensorFlow模型的智能移动apps。您将了解如何使用循序渐进的教程快速构建这样的app，以及如何利用大量来之不易的故障排除技巧来避免开发过程中的许多陷阱。

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 Locati***

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 *** commands recognition model

Using a *** speech recognition model in Android

Building a new app using the model

Showing model-powered recognition results

Using a *** 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 *** 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 R***berry Pi

Setting up R***berry Pi and *** it move

Setting up R***berry Pi

Making R***berry Pi move

Setting up TensorFIow on R***berry Pi

Image recognition and text to speech

Audio recognition and robot movement

Reinforcement learning on R***berry 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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