Intro to Deep Learning and Generative Models Course - L5.4 (Optional) Calculus Refresher I: Derivatives

Sebastian's books: https://sebastianraschka.com/books/ Understanding gradient descent, which is an optimization algorithm for minimizing the loss of a predictive model, requires some calculus basics. Since we all have varying calculus backgrounds, let me introduce or recap some calculus tidbits that will be useful for understanding gradient descent. In this video, I will start by talking about derivatives (you can think of them as something like 1-dimensional gradients). Slides: https://sebastianraschka.com/pdf/lecture-notes/stat453ss21/L05_gradient-descent_slides.pdf ------- This video is part of my Introduction of Deep Learning course. Next video: https://youtu.be/YPZVGSRmjLk The complete playlist: https://www.youtube.com/playlist?list=PLTKMiZHVd_2KJtIXOW0zFhFfBaJJilH51 A handy overview page with links to the materials: https://sebastianraschka.com/blog/2021/dl-course.html ------- If you want to be notified about future videos, please consider subscribing to my channel: https://youtube.com/c/SebastianRaschka