Uploaded February 2021 | Updated September 2026, 2 weeks ago
Sebastian's books: sebastianraschka.com/books
In the previous videos, we learned about one of the coolest features of PyTorch: automatic differentiation (that is, automatically computing gradients for us). Now, PyTorch also offers many convenience tools and functionality that makes deep learning easier for us by abstracting away the complicated bits. In this video, we will take a look at how we can use the PyTorch API from a objected-oriented and a more functional perspective.
Slides: sebastianraschka.com/pdf/lecture-notes/stat453ss21/L06_pytorch_slides.pdf
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This video is part of my Introduction of Deep Learning course.
Next video: youtu.be/5pew4YEa1ww
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
Sebastian's books: sebastianraschka.com/books
In the previous videos, we learned about one of the coolest features of PyTorch: automatic differentiation (that is, automatically computing gradients for us). Now, PyTorch also offers many convenience tools and functionality that makes deep learning easier for us by abstracting away the complicated bits. In this video, we will take a look at how we can use the PyTorch API from a objected-oriented and a more functional perspective.
Slides: sebastianraschka.com/pdf/lecture-notes/stat453ss21/L06_pytorch_slides.pdf
-------
This video is part of my Introduction of Deep Learning course.
Next video: youtu.be/5pew4YEa1ww
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
