Practical AI on the Google Cloud Platform by Lanham, Micheal -- Read -- Imperial Library of Trantor

Index

Preface

Who Should Read This Book Why I Wrote This Book Navigating This Book A Note on the Google AI Platform Things You Need for This Book Conventions Used in This Book Using Code Examples O’Reilly Online Learning How to Contact Us Acknowledgments

1. Data Science and Deep Learning

What Is Data Science? Classification and Regression

Regression Goodness of Fit Classification with Logistic Regression Multivariant Regression and Classification

Data Discovery and Preparation

Bad Data Training, Test, and Validation Data Good Data Preparing Data Questioning Your Data

The Basics of Deep Learning

The Perceptron Game

Understanding How Networks Learn

Backpropagation Optimization and Gradient Descent Vanishing or Exploding Gradients SGD and Batching Samples Batch Normalization and Regularization Activation Functions Loss Functions

Building a Deep Learner

Optimizing a Deep Learning Network Overfitting and Underfitting Network Capacity

Conclusion

Game Answers

2. AI on the Google Cloud Platform

AI Services on GCP

The AI Hub AI Platform AI Building Blocks

Google Colab Notebooks

Building a Regression Model with Colab

AutoML Tables The Cloud Shell Managing Cloud Data Conclusion

3. Image Analysis and Recognition on the Cloud

Deep Learning with Images

Enter Convolution Neural Networks

Image Classification

Set Up and Load Data Inspecting Image Data Channels and CNN Building the Model Training the AI Fashionista to Discern Fashions Improving Fashionista AI 2.0

Transfer Learning Images

Identifying Cats or Dogs Transfer Learning a Keras Application Model Training Transfer Learning Retraining a Better Base Model

Object Detection and the Object Detection Hub API

YOLO for Object Detection

Generating Images with GANs Conclusion

4. Understanding Language on the Cloud

Natural Language Processing, with Embeddings

Understanding One-Hot Encoding Vocabulary and Bag-of-Words Word Embeddings Understanding and Visualizing Embeddings

Recurrent Networks for NLP

Recurrent Networks for Memory Classifying Movie Reviews RNN Variations

Neural Translation and the Translation API

Sequence-to-Sequence Learning Translation API AutoML Translation

Natural Language API BERT: Bidirectional Encoder Representations from Transformers

Semantic Analysis with BERT Document Matching with BERT BERT for General Text Analysis

Conclusion

5. Chatbots and Conversational AI

Building Chatbots with Python Developing Goal-Oriented Chatbots with Dialogflow Building Text Transformers

Loading and Preparing Data Understanding Attention Masking and the Transformer Encoding and Decoding the Sequence

Training Conversational Chatbots

Compiling and Training the Model Evaluation and Prediction

Using Transformer for Conversational Chatbots Conclusion

6. Video Analysis on the Cloud

Downloading Video with Python Video AI and Video Indexing Building a Webcam Face Detector

Understanding Face Embeddings

Recognizing Actions with TF Hub Exploring the Video Intelligence API Conclusion

7. Generators in the Cloud

Unsupervised Learning with Autoencoders

Mapping the Latent Space with VAE

Generative Adversarial Network Exploring the World of Generators

A Path for Exploring GANs Translating Images with Pix2Pix and CycleGAN

Attention and the Self-Attention GAN

Understanding Self-Attention Self-Attention for Image Colorization—DeOldify

Conclusion

8. Building AI Assistants in the Cloud

Needing Smarter Agents Introducing Reinforcement Learning

Multiarm Bandits and a Single State Adding Quality and Q Learning Exploration Versus Exploitation Understanding Temporal Difference Learning

Building an Example Agent with Expected SARSA

Using SARSA to Drive a Taxi Learning State Hierarchies with Hierarchical Reinforcement Learning

Bringing Deep to Reinforcement Learning

Deep Q Learning Optimizing Policy with Policy Gradient Methods

Conclusion

9. Putting AI Assistants to Work

Designing an Eat/No Eat AI Selecting and Preparing Data for the AI Training the Nutritionist Model Optimizing Deep Reinforcement Learning Building the Eat/No Eat Agent Testing the AI Agent Commercializing the AI Agent

Identifying App/AI Issues Involving Users and Progressing Development

Future Considerations Conclusion

10. Commercializing AI

The Ethics of Commercializing AI Packaging Up the Eat/No Eat App Reviewing Options for Deployment

Deploying to GitHub Deploying with Google Cloud Deploy

Exploring the Future of Practical AI Conclusion

Index

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