Intelligent Mobile Projects with TensorFlow Build 10+ Artificial Intelligence Apps Using TensorFlow Mobile and Lite for IOS, Android, and Raspberry Pi
Google TensorFlow is used to train all the models deployed and running on mobile devices. This book covers 10 projects on the implementation of all major AI areas on iOS, Android, and Raspberry Pi: computer vision, speech and language processing, and machine learning, including traditional, reinforc...
Uloženo v:
| Hlavní autor: | |
|---|---|
| Médium: | E-kniha |
| Jazyk: | angličtina |
| Vydáno: |
Birmingham
Packt Publishing, Limited
2018
Packt Publishing Limited Packt Publishing |
| Vydání: | 1 |
| Témata: | |
| ISBN: | 9781788834544, 1788834542 |
| On-line přístup: | Získat plný text |
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- 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
- 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 -- Summary -- 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 -- 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 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 -- 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 iOS 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 iOS 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 iOS -- 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 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 -- Summary -- 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 -- Summary -- 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 -- 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 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 -- Summary -- 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
- Intelligent Mobile Projects with TensorFlow: Build 10+ Artificial Intelligence apps using TensorFlow Mobile and Lite for iOS, Android, and Raspberry Pi