Sebastian's books: https://sebastianraschka.com/books/
As previously mentioned, PyTorch can compute gradients automatically for us. In order to do that, it tracks computations via a computation graph, and then when it is time to compute the gradient, it moves backward along the computation graph. Actually, computations graphs are also a helpful concept for learning how differentiation (computing partial derivatives and gradients) work, which is what we are doing in this video.
Slides: https://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: https://youtu.be/VvUz0Q9e09g
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
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