TensorFlow for Deep Learning

Index

Symbols

• 2D convolutions, TensorFlow Convolutional Primitives

A

• a.eval(), Initializing Constant Tensors
• A3C algorithm
  • A3C loss function, The A3C Loss Function
  • defining workers, Defining Workers
  • overview of, The A3C Algorithm
  • training the policy, Training the Policy
• A3C.fit() method, Training the Policy
• accuracy, Binary Classification Metrics
• accuracy_score(), Evaluating Model Accuracy
• acknowledgments, Acknowledgments
• actions, Reinforcement Learning
• activations, Activations
• adding tensors, Tensor Addition and Scaling
• add_output() method, Defining a Graph of Layers
• advantage functions, Policy Learning
• adversarial models, Adversarial models
• agents, Markov Decision Processes
• AI winters, “Neurons” in Fully Connected Networks
• AlexNet, AlexNet, Convolutional Neural Networks
• algorithms
  • A3C algorithm, The A3C Algorithm-The A3C Algorithm
  • asynchronous training, Asynchronous Training
  • black-box, Hyperparameter Optimization Algorithms
  • catastrophic forgetting and, Q-Learning
  • finding baselines, Setting Up a Baseline
  • for reinforcement learning, Reinforcement Learning Algorithms-Asynchronous Training
  • graduate student descent, Graduate Student Descent
  • grid search, Grid Search
  • policy learning, Policy Learning
  • Q-learning, Q-Learning
  • random hyperparameter search, Random Hyperparameter Search
• AlphaGo, AlphaGo, Reinforcement Learning, Markov Decision Processes, Limits of Reinforcement Learning, The A3C Algorithm
• Anaconda Python, Installing TensorFlow and Getting Started, Installing DeepChem
• architectural primitives, Deep Learning Primitives ...