
Practical AI on the Google Cloud Platform
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Content
- Intro
- Copyright
- Table of Contents
- 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
- Chapter 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
- Chapter 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
- Chapter 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
- Chapter 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
- Chapter 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
- Chapter 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
- Chapter 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
- Chapter 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
- Chapter 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
- Chapter 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
- About the Author
- Colophon
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