Uploaded February 2021 | Updated September 2026, 2 weeks ago
Sebastian's books: sebastianraschka.com/books
In the context of deep learning, you will often stumble upon terms such as "logits" and "cross entropy". As we will see in this video, these are not new concepts but are something that we can derive from our understanding of logistic regression. In fact, the logits are what we are already computing in logistic regression. And the cross entropy is essentially equivalent to the logistic regression loss function -- you can think of it as just another name.
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/6igMArA6k3A
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 context of deep learning, you will often stumble upon terms such as "logits" and "cross entropy". As we will see in this video, these are not new concepts but are something that we can derive from our understanding of logistic regression. In fact, the logits are what we are already computing in logistic regression. And the cross entropy is essentially equivalent to the logistic regression loss function -- you can think of it as just another name.
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/6igMArA6k3A
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
