Uploaded February 2021 | Updated September 2026, 2 weeks ago
Sebastian's books: sebastianraschka.com/books
Now that we understand the forward pass in logistic regression and are familiar with the loss function, let us look at the loss derivative (or gradient) with respect to the weights. Then we can apply gradient descent to update the weights to minimize the loss and thereby optimize the prediction accuracy.
Slides: sebastianraschka.com/pdf/lecture-notes/stat453ss21/L08_logistic__slides.pdf
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This video is part of my Introduction of Deep Learning course.
Next video: youtu.be/icQaFxKa_J0
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
Now that we understand the forward pass in logistic regression and are familiar with the loss function, let us look at the loss derivative (or gradient) with respect to the weights. Then we can apply gradient descent to update the weights to minimize the loss and thereby optimize the prediction accuracy.
Slides: sebastianraschka.com/pdf/lecture-notes/stat453ss21/L08_logistic__slides.pdf
-------
This video is part of my Introduction of Deep Learning course.
Next video: youtu.be/icQaFxKa_J0
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
