This lecture provides an introduction to supervised learning, where a database with labeled data points is used to optimize the output of a classifier by minimizing errors through parameter adjustments.
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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 CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.
