Covers Principal Component Analysis for dimensionality reduction, exploring its applications, limitations, and importance of choosing the right components.
Covers CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.
Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.