Lectures related to Proximal Gradient Descent: Optimization Techniques in Machine Learning

Neural networks under SGD Log in to Mediaspace to watch this video

Explores neural networks trained under Stochastic Gradient Descent, discussing hidden layers, square loss function, and particle evolution.

Linear Programming: Solving LPs Log in to Mediaspace to watch this video

Covers the process of solving Linear Programs (LPs) using the simplex method.

Lasso and MNIST Basics Log in to Mediaspace to watch this video

Introduces Lasso regularization and its application to the MNIST dataset, emphasizing feature selection and practical exercises on gradient descent implementation.

Dynamic Programming: How Many Ways to Make Change Log in to Mediaspace to watch this video

Demonstrates dynamic programming to find the number of ways to make change using different coin denominations.

Convexifying Nonconvex Problems: SVM and Dimensionality Reduction Log in to Mediaspace to watch this video

Explores convexifying nonconvex problems through SVM and dimensionality reduction techniques.