This lecture introduces the concept of policy gradients, explaining how actions are associated with observations to optimize rewards parametrically using a gradient method, contrasting it with Q-learning.
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Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.
Covers the foundational concepts of deep learning and the Transformer architecture, focusing on neural networks, attention mechanisms, and their applications in sequence modeling tasks.
