Abstract
In this talk, we will explore the mathematical principles behind machine learning, focusing on Gradient Descent and Backpropagation. We will examine Gradient Descent as an optimization method for minimizing loss functions, addressing its convergence and challenges. The Backpropagation algorithm will be introduced as a framework for efficiently computing gradients in deep neural networks. Combining theoretical insights with practical examples, the seminar highlights how these algorithms enable machines to model complex data patterns.