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Deep Learning with TensorFlow and Keras - 3rd Edition : Build and Deploy Supervised, Unsupervised, Deep, and Reinforcement Learning Models. by Kapoor, Amita

Table of Contents: “…-- How to deal with the highly correlated input state space -- How to deal with the problem of moving targets -- Reinforcement success in recent years -- Simulation environments for RL -- An introduction to OpenAI Gym -- Random agent playing Breakout -- Wrappers in Gym -- Deep Q-networks -- DQN for CartPole -- DQN to play a game of Atari -- DQN variants -- Double DQN -- Dueling DQN -- Rainbow -- Deep deterministic policy gradient -- Summary -- References -- Chapter 12: Probabilistic TensorFlow -- TensorFlow Probability -- TensorFlow Probability distributions -- Using TFP distributions -- Coin Flip Example -- Normal distribution -- Bayesian networks -- Handling uncertainty in predictions using TensorFlow Probability -- Aleatory uncertainty -- Epistemic uncertainty -- Creating a synthetic dataset -- Building a regression model using TensorFlow -- Probabilistic neural networks for aleatory uncertainty -- Accounting for the epistemic uncertainty -- Summary -- References.…”
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