Gradient Descent Methods for Artificial Neural Networks

This lecture covers the principles of gradient descent, focusing on its application in training artificial neural networks. Starting with the basics of supervised learning and single-layer networks, the instructor explains the limitations of simple perceptrons and introduces the concept of multi-layer networks. The lecture delves into the challenges of training deep networks, including the issues of overfitting and generalization. Modern gradient descent methods such as batch, online, and minibatch rules are discussed, along with their properties and convergence criteria. The session concludes with a quiz to test understanding of the material.