Practical AI on the Google Cloud Platform

Index

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• action recognition model, Recognizing Actions with TF Hub-Recognizing Actions with TF Hub
• action-values, Choosing the greedy action
• activation functions, The Basics of Deep Learning, Activation Functions
• AdaDelta optimizer, Optimization and Gradient Descent
• AdaGrad optimizer, Optimization and Gradient Descent
• AdaMax optimizer, Optimization and Gradient Descent
• Adaptive Moment Estimation (Adam) optimizer, Optimization and Gradient Descent
• adversarial learning, Classification and Regression, Loss Functions, Generators in the Cloud, Unsupervised Learning with Autoencoders (see also generative adversarial networks (GANs))
• agent state, Learning State Hierarchies with Hierarchical Reinforcement Learning
• agents, definition of term, Needing Smarter Agents
• Agile development, Commercializing the AI Agent
• AI assistants (see also Eat/No Eat agent assistant)
  • building
    • deep reinforcement learning, Bringing Deep to Reinforcement Learning-Optimizing Policy with Policy Gradient Methods
    • example agent, Building an Example Agent with Expected SARSA-Functional decomposition with MAXQ
    • need for smarter agents, Needing Smarter Agents
    • overview of, Building AI Assistants in the Cloud
    • reinforcement learning and, Introducing Reinforcement Learning-Discrete versus continuous actions or states
  • commercializing
    • deployment options, Reviewing Options for Deployment-Deploying with Google Cloud Deploy
    • ethics of, The Ethics of Commercializing AI
    • future of practical AI, Exploring the Future of Practical AI-Exploring the Future of ...