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Intelligent Mobile Projects with TensorFlow

By : Tang

5 (4)

Intelligent Mobile Projects with TensorFlow

5 (4)

By: Tang

Overview of this book

As a developer, you always need to keep an eye out and be ready for what will be trending soon, while also focusing on what's trending currently. So, what's better than learning about the integration of the best of both worlds, the present and the future? Artificial Intelligence (AI) is widely regarded as the next big thing after mobile, and Google's TensorFlow is the leading open source machine learning framework, the hottest branch of AI. This book covers more than 10 complete iOS, Android, and Raspberry Pi apps powered by TensorFlow and built from scratch, running all kinds of cool TensorFlow models offline on-device: from computer vision, speech and language processing to generative adversarial networks and AlphaZero-like deep reinforcement learning. You’ll learn how to use or retrain existing TensorFlow models, build your own models, and develop intelligent mobile apps running those TensorFlow models. You'll learn how to quickly build such apps with step-by-step tutorials and how to avoid many pitfalls in the process with lots of hard-earned troubleshooting tips.

Preface

Preface

Who this book is for

What this book covers

To get the most out of this book

Get in touch

Free Chapter

Getting Started with Mobile TensorFlow

Getting Started with Mobile TensorFlow

Setting up TensorFlow

Setting up Xcode

Setting up Android Studio

TensorFlow Mobile vs TensorFlow Lite

Running sample TensorFlow iOS apps

Running sample TensorFlow Android apps

Summary

Classifying Images with Transfer Learning

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 TensorFlow to your own iOS app

Adding TensorFlow to your own Android app

Summary

Detecting Objects and Their Locations

Detecting Objects and Their Locations

Object detection–a quick overview

Setting up the TensorFlow Object Detection API

Retraining SSD-MobileNet and Faster RCNN models

Using object detection models in iOS

Using YOLO2–another object-detection model

Summary

Transforming Pictures with Amazing Art Styles

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 iOS

Using fast neural-style transfer models in Android

Using the TensorFlow Magenta multi-style model in iOS

Using the TensorFlow Magenta multi-style model in Android

Summary

Understanding Simple Speech Commands

Understanding Simple Speech Commands

Speech recognition – a quick overview

Training a simple commands recognition model

Using a simple speech recognition model in Android

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

Using a simple speech recognition model in iOS with Swift

Summary

Describing Images in Natural Language

Describing Images in Natural Language

Image captioning – how it works

Training and freezing an image captioning model

Transforming and optimizing the image captioning model

Using the image captioning model in iOS

Using the image captioning model in Android

Summary

Recognizing Drawing with CNN and LSTM

Recognizing Drawing with CNN and LSTM

Drawing classification – how it works

Training, predicting, and preparing the drawing classification model

Using the drawing classification model in iOS

Using the drawing classification model in Android

Summary

Predicting Stock Price with RNN

Predicting Stock Price with RNN

RNN and stock price prediction – what and how

Using the TensorFlow RNN API for stock price prediction

Using the Keras RNN LSTM API for stock price prediction

Running the TensorFlow and Keras models on iOS

Running the TensorFlow and Keras models on Android

Summary

Generating and Enhancing Images with GAN

Generating and Enhancing Images with GAN

GAN – what and why

Building and training GAN models with TensorFlow

Using the GAN models in iOS

Using the GAN models in Android

Summary

Building an AlphaZero-like Mobile Game App

Building an AlphaZero-like Mobile Game App

AlphaZero – how does it work?

Training and testing an AlphaZero-like model for Connect 4

Using the model in iOS to play Connect 4

Using the model in Android to play Connect 4

Summary

Using TensorFlow Lite and Core ML on Mobile

Using TensorFlow Lite and Core ML on Mobile

TensorFlow Lite – an overview

Using TensorFlow Lite in iOS

Using TensorFlow Lite in Android

Core ML for iOS – an overview

Using Core ML with Scikit-Learn machine learning

Using Core ML with Keras and TensorFlow

Summary

Developing TensorFlow Apps on Raspberry Pi

Developing TensorFlow Apps on Raspberry Pi

Setting up Raspberry Pi and making it move

Setting up TensorFlow on Raspberry Pi

Image recognition and text to speech

Audio recognition and robot movement

Reinforcement learning on Raspberry Pi

Summary

Final words

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Building an AlphaZero-like Mobile Game App

Although the ever-increasing popularity of modern Artificial Intelligence (AI) was essentially caused by the breakthrough of deep learning in 2012, the historic events of Google DeepMind's AlphaGo beating Lee Sedol, the 18-time world champion of GO, 4-1 in March 2016 and then beating Ke Jie, the current #1-ranked GO player, 3-0 in May 2017, contributed in large part to making AI a household acronym. Due to the complexity of the GO game, it was wildly considered an impossible mission, or impossible for at least one more decade, that a computer program would beat top GO players.

After the match between AlphaGo and Ke Jie in May 2017, Google retired AlphaGo; DeepMind, the startup Google acquired for its pioneering deep reinforcement learning technologies and the developer of AlphaGo, decided to focus their AI research on other areas...

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