Learning Agents: Exploration-Exploitation Tradeoff

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Explains the significance of mini-batches in Deep Reinforcement Learning and the differences between on-policy and off-policy methods.

Delves into the impact of deep learning on non-conceptual knowledge systems and the advancements in transformers and generative adversarial networks.

Explores the Random Feature Method for solving PDEs using machine learning algorithms to approximate high-dimensional functions efficiently.

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

Covers Convolutional Neural Networks, standard architectures, training techniques, and adversarial examples in deep learning.

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

Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.

Covers the fundamentals of deep learning, from artificial neurons to modern networks.