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Delves into Reinforcement Learning with Human Feedback, discussing convergence of estimators and introducing a pessimistic approach for improved performance.
Covers the fundamentals of multilayer neural networks and deep learning, including back-propagation and network architectures like LeNet, AlexNet, and VGG-16.
Explores training robots through reinforcement learning and learning from demonstration, highlighting challenges in human-robot interaction and data collection.
