Practical AI on the Google Cloud Platform

Spis treści książki

• 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
  • OReilly 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
      • Game 5
• 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
      • Translating unpaired images with CycleGAN
  • Attention and the Self-Attention GAN
    • Understanding Self-Attention
    • Self-Attention for Image ColorizationDeOldify
  • Conclusion
• 8. Building AI Assistants in the Cloud
  • Needing Smarter Agents
  • Introducing Reinforcement Learning
    • Multiarm Bandits and a Single State
      • Choosing the greedy action
    • Adding Quality and Q Learning
      • Playing with OpenAI Gym
    • Exploration Versus Exploitation
      • Balancing exploration and exploitation
    • Understanding Temporal Difference Learning
      • Episodic versus continuous learning
      • On policy versus off policy
      • Model-based versus model-free
      • Discrete versus continuous actions or states
  • Building an Example Agent with Expected SARSA
    • Using SARSA to Drive a Taxi
      • Understanding hierarchical states
    • Learning State Hierarchies with Hierarchical Reinforcement Learning
      • Functional decomposition with MAXQ
  • 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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