Gradient Descent: MNIST Dataset and Logistic Loss

This lecture covers the implementation of gradient descent algorithms from scratch using the MNIST dataset in machine learning. The instructor explains how to load the dataset, preprocess the images, split the data, and convert the multi-class classification problem into a binary classification task. The lecture also delves into the logistic loss function, its gradient computation, and the iterative process of updating weights to minimize the loss. Various stopping criteria for convergence are discussed, along with the importance of differentiability in the function for gradient descent. The session concludes with a detailed explanation of the logistic loss and binary cross entropy loss for classification tasks.