Intro to Deep Learning and Generative Models Course - L6.3 Automatic Differentiation in PyTorch -- Code Example

Sebastian's books: https://sebastianraschka.com/books/ In the previous video, we learned about computation graphs and how we can use them to compute partial derivatives and gradients conveniently. This video is the practical companion to the previous video, which explained the broader concepts. Now, we will see how this concepts come together in practice in PyTorch. Slides: https://sebastianraschka.com/pdf/lecture-notes/stat453ss21/L06_pytorch_slides.pdf Code: https://github.com/rasbt/stat453-deep-learning-ss21/blob/main/L06/code/pytorch-autograd.ipynb ------- This video is part of my Introduction of Deep Learning course. Next video: https://youtu.be/00KgeJwNaZA 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