Understanding Binary Cross-Entropy / Log Loss in 5 minutes: a visual explanation
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- Опубліковано 22 сер 2024
- If you are training a binary classifier, chances are you are using binary cross-entropy / log loss as your loss function.
Have you ever thought about what exactly does it mean to use this loss function? This video explains the concepts behind binary cross-entropy / log loss in a visually clear and concise manner.
The content of this video is also available in the corresponding blog post: towardsdatasci...
thank you so much! the blog post that bought me here was phenomenal!
Great video, but in case anyone else is confused: 3:31 should already have the minus signs outside log (i.e. -log) to match with the on-screen diagram.
yess
I agree
Simply amazing. Thank you for this much needed refresher
Thank you. Please release more videos in such format.
best explanation by far. great effort making the animations
Thank you, Sir, for this brilliant video and the corresponding blog post.
This is super content - really nicely done! You should do more of these - this one was great, I'd love to see more (I subscribed). The equation colouring and mapping to the visual is *so incredibly useful* (looks like 3b1b's Manim?).
neat and brief. Than you
Well explained! Good Job!
Thank you!
Smooooch )
Great explanation. The animations are a bit distracting and also you could keep the bars up when you show the formula, would be helpful! Otherwise brilliant!!
chat gpt brought me here . Lets goo