[DL] Categorial cross-entropy loss (softmax loss) for multi-class classification 

This was unbelievably helpful!! thank you!! very clear explanation!!

Nice explanation. Although few words to highlight some key points regarding the topic. - We take the exponential function while calculating the probablities f(s)i because any returned function score could be negative and the exponential helps to avoid that. While dividing by the sum of exponentials is done in order to make the probabilities sum equal to 1 (normalization). - The logarithmic function is used to calculate cross entropy for given proabilities because products are expensive and log helps to convert product to sum function. Again negative sign is used before sum because log for values between 0 and 1 results negative values.

excellent and consice mathematical explanation backed up by a numerical example ,Thankyou

Thank you so much for this good explanation

Thank you, this helps me a lot!

well explained!

well explained.

Character In the video It's great, I like it a lot $$

I've only one question, if the network wrongly classifies a 0 1 0 (one hot) to (0 0.56 0.44) so how cross entropy loss is penalizing for 0 represented as 0.44? is it neglecting?