Deep Learning Agents: Reinforcement Learning

Related lectures (29)

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Introduces Convolutional Neural Networks (CNNs) for autonomous vehicles, covering architecture, applications, and regularization techniques.

Explores the exploration-exploitation tradeoff in learning unknown effects of actions using multi-armed bandits and Q-learning.

Discusses reinforcement learning, focusing on temporal difference learning and SARSA algorithm variations.

Covers stylized facts of asset returns, summary statistics, testing for normality, Q-Q plots, and efficient market hypothesis.

Explores artificial neural networks, reward information in the brain, animal conditioning, deep reinforcement learning, and a quiz on rewards.

Explores neural networks optimization, including backpropagation, batch normalization, weight initialization, and hyperparameter search strategies.

Explores the gap between AI and human intelligence through neuroscience-inspired models and algorithms.

Explores learning in deep learning for autonomous vehicles, covering predictive models, RNN, ImageNet, and transfer learning.

Provides insights on gradient-based algorithms, deep learning mysteries, and the challenges of non-convex problems.