Neural Networks: Learning Features & Linear Prediction

This lecture introduces neural networks as a way to learn features directly from observations and make linear predictions on top of these learned features. The instructor explains how neural networks can fix the limitations of kernel methods by allowing the learning of various features. The lecture covers the representation power of neural networks, their application in deep learning, and the importance of having a large amount of data for effective performance. The instructor also discusses the structure of neural networks, the role of activation functions like sigmoid and ReLU, and the approximation of functions using piecewise linear functions and ReLU activations.