Logistic regression 5.2: Multiclass - Softmax regression

[@ptnpdrs2001]

Fantastic explanation, thanks a lot Herman!

[@blessontomjoseph]

the whole logistic regression playlist is o good. great job!

[@kamperh]

Very happy it helps!

[@DimiqBaba]

Your videos are quite easy to comprehend. Good job.

[@gautamkulkarni7049]

Thank you for explaining the whole concept in a much simpler way.

[@mohamadalipour2563]

thank you for the explanation, Better than the professor at University ! ...

[@catdonut6696]

Very clear and concrete explanation! Thank you so much.

[@kamperh]

Very big pleasure! :D

[@user-bn7ms7it2c]

love it! so well explained and love the mathmetical proof

[@Nanona6]

Thank you very much, it helped me a lot for my university classes. Very clear explanation. :)

[@kamperh]

Very happy it helped!! :D

[@lima073]

This is a remarkable explanation, simple and complete, thank you very much !

[@norebar5848]

What an awesome video, thank you! I made sure to watch lots of ads for you, even though it probably doesnt make a difference :D

[@kamperh]

Thanks for the encouragement!! :)

[@amgadshrief7215]

Very good Explanation :)

[@arvindkanesan]

Very well explained!

[@somnathchatterjee9601]

Great work man!!

[@Charles_Reid]

Thanks, this is a very helpful video. One question, in the video you mentioned that since probability is between 0 and 1 and probabilities sum to 1, you need to raise e to the power of each score and divide by the sum of the scores to obtain a probability. Is there a reason that you choose e as the base of the exponent? Why not choose another number? My confusion is that if I chose a number like 10 as the base, I'm pretty sure my softmax model would classify everything the same as if I had chosen e, but that the probabilities calculated would be different. I'm wondering if softmax is actually returning the real probability, or just a number between 0 and1 that behaves like the real probability. Thanks!

[@kamperh]

This is a really good question that I hadn't thought about before. First, using base 10 will probably work fine because of all the reasons you say. If you were training a neural network, you could probably use any number and the network would just adjust the logits to do what it must do. I see there are some practical reasons to use e: forums.fast.ai/t/why-does-softmax-use-e/78118 And finally I want to ask tongue-in-cheek: What does it mean when you say "real probability"? : ) No one knows the real probability except the Creator, and all we're doing is trying to model it ;)

[@Charles_Reid]

@@kamperh Yeah maybe the "real probability" can only be 0 or 1, as the data point either does belong to the class or does not. But we don't know which class it belongs to, so SoftMax gives us a probability that is different from the so-called "real probability" but that helps us make a guess. Thank you for your help!

[@pawestelmasik9998]

Great job

[@Storyvibe69]

nicely explained sherlock homes

[@kamperh]

I'm a big fan of the Sherlock books and series, so I'll take it as a compliment :P

[@israalewaa1480]

Prof. Herman, i have an issue for this can i sent to your email please ?