Perception: Data-Driven Approaches

This lecture focuses on perception in the context of deep learning for autonomous vehicles. It covers topics such as image classification, parametric methods, score and loss functions, optimization methods, and neural networks. The instructor explains the importance of learning rates, stochastic gradient descent, and popular optimization methods like momentum update and Adagrad. The lecture delves into the role of representation in machine learning, comparing hand-designed features with learned representations using neural networks. It concludes with insights on deep learning intuitions and the current state-of-the-art in deep learning architectures.