Intelligent mobile projects with TensorFlow : build 10+ artificial intelligence apps using TensorFlow mobile and Lite for iOS, android, and raspberry Pi / Jeff Tang.

Format:

Book

Author/Creator:

Tang, Jeff, author.

Language:

English

Subjects (All):

Raspberry Pi (Computer)--Programming.

Raspberry Pi (Computer).

Physical Description:

1 online resource (1 volume) : illustrations

Edition:

1st edition

Place of Publication:

Birmingham ; Mumbai : Packt Publishing, 2018.

System Details:

text file

Biography/History:

Tang Jeff: Jeff Tang fell in love with classical AI more than two decades ago. After his MS in CS, he worked on Machine Translation for 2 years and then, to survive the long AI winter, he worked on enterprise apps, voice apps, web apps, and mobile apps at startups, AOL, Baidu, and Qualcomm. He developed a top-selling iOS app with millions of downloads and was recognized by Google as a Top Android Market Developer. He reconnected with modern AI in 2015 and knew that AI will be his passion and commitment for the next two decades. One of his favorite topics is to make AI available anytime anywhere and hence the book.

Summary:

Create Deep Learning and Reinforcement Learning apps for multiple platforms with TensorFlow About This Book Build TensorFlow-powered AI applications for mobile and embedded devices Learn modern AI topics such as computer vision, NLP, and deep reinforcement learning Get practical insights and exclusive working code not available in the TensorFlow documentation Who This Book Is For If you're an iOS/Android developer interested in building and retraining others' TensorFlow models and running them in your mobile apps, or if you're a TensorFlow developer and want to run your new and amazing TensorFlow models on mobile devices, this book is for you. You'll also benefit from this book if you're interested in TensorFlow Lite, Core ML, or TensorFlow on Raspberry Pi. What You Will Learn Classify images with transfer learning Detect objects and their locations Transform pictures with amazing art styles Understand simple speech commands Describe images in natural language Recognize drawing with Convolutional Neural Network and Long Short-Term Memory Predict stock price with Recurrent Neural Network in TensorFlow and Keras Generate and enhance images with generative adversarial networks Build AlphaZero-like mobile game app in TensorFlow and Keras Use TensorFlow Lite and Core ML on mobile Develop TensorFlow apps on Raspberry Pi that can move, see, listen, speak, and learn In Detail 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. Style and a...

Contents:

Cover

Copyright and Credits

Dedication

Packt Upsell

Foreword

Contributors

Table of Contents

Preface

Chapter 1: Getting Started with Mobile TensorFlow

Setting up TensorFlow

Setting up TensorFlow on MacOS

Setting up TensorFlow on GPU-powered Ubuntu

Setting up Xcode

Setting up Android Studio

TensorFlow Mobile vs TensorFlow Lite

Running sample TensorFlow iOS apps

Running sample TensorFlow 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 TensorFlow to your own iOS app

Adding TensorFlow to your Objective-C iOS app

Adding TensorFlow to your Swift iOS app

Adding TensorFlow to your own Android app

Chapter 3: Detecting Objects and Their Locations

Object detection-a quick overview

Setting up the TensorFlow 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 TensorFlow iOS libraries manually

Using TensorFlow iOS libraries in an app

Adding an object detection feature to an iOS app

Using YOLO2-another object-detection model

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 iOS

Adding and testing with fast neural transfer models

Looking back at the iOS code using fast neural transfer models

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

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 iOS with Objective-C

Fixing model-loading errors with tf_op_files.txt

Using a simple speech recognition model in iOS with Swift

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 iOS

Using the image captioning model in Android

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 iOS

Building custom TensorFlow library for iOS

Developing an iOS app to use the model

Using the drawing classification model in Android

Building custom TensorFlow library for Android

Developing an Android app to use the model

Chapter 8: Predicting Stock Price with RNN

RNN and stock price prediction - what and how

Using the TensorFlow RNN API for stock price prediction

Training an RNN model in TensorFlow

Testing the TensorFlow 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 TensorFlow and Keras models on iOS

Running the TensorFlow and Keras models on Android

Chapter 9: Generating and Enhancing Images with GAN

GAN - what and why

Building and training GAN models with TensorFlow

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

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

Chapter 11: Using TensorFlow Lite and Core ML on Mobile

TensorFlow Lite - an overview

Using TensorFlow Lite in iOS

Running the example TensorFlow Lite iOS apps

Using a prebuilt TensorFlow Lite model in iOS

Using a retrained TensorFlow model for TensorFlow Lite in iOS

Using a custom TensorFlow Lite model in iOS

Using TensorFlow 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 TensorFlow

Chapter 12: Developing TensorFlow Apps on Raspberry Pi

Setting up Raspberry Pi and making it move

Setting up Raspberry Pi

Making Raspberry Pi move

Setting up TensorFlow 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

Final words

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Index.

Notes:

Description based on print version record.

ISBN:

9781788628808

1788628802

OCLC:

1040599140