Uploaded February 2021 | Updated September 2026, 2 weeks ago
Sebastian's books: 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: sebastianraschka.com/pdf/lecture-notes/stat453ss21/L06_pytorch_slides.pdf
Code: github.com/rasbt/stat453-deep-learning-ss21/blob/main/L06/code/pytorch-autograd.ipynb
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This video is part of my Introduction of Deep Learning course.
Next video: youtu.be/00KgeJwNaZA
The complete playlist: youtube.com/playlist?list=PLTKMiZHVd_2KJtIXOW0zFhFfBaJJilH51
A handy overview page with links to the materials: sebastianraschka.com/blog/2021/dl-course.html
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If you want to be notified about future videos, please consider subscribing to my channel: youtube.com/c/SebastianRaschka
Sebastian's books: 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: sebastianraschka.com/pdf/lecture-notes/stat453ss21/L06_pytorch_slides.pdf
Code: 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: youtu.be/00KgeJwNaZA
The complete playlist: youtube.com/playlist?list=PLTKMiZHVd_2KJtIXOW0zFhFfBaJJilH51
A handy overview page with links to the materials: sebastianraschka.com/blog/2021/dl-course.html
-------
If you want to be notified about future videos, please consider subscribing to my channel: youtube.com/c/SebastianRaschka
