Lasso and MNIST Basics

This lecture covers the basics of Lasso regularization and its application to the MNIST dataset. The instructor explains the process of rescaling pixel values, reshaping images, and relabeling data to convert a multi-class classification problem into a binary one. The importance of understanding dataset features, splitting data into train, validation, and test sets, and the use of pandas for saving results are emphasized. The lecture also delves into the differences between L1 and L2 regularization, showcasing how Lasso performs feature selection by pushing irrelevant features to zero. Practical exercises on implementing gradient descent and coding Lasso regularization are assigned to students.