Softmax Function in Deep Learning

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In mathematics, the softmax function, also known as softargmax or normalized exponential function, is a function that takes as input a vector z of K real numbers, and normalizes it into a probability distribution consisting of K probabilities proportional to the exponentials of the input numbers.

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The most simplest explanation of softmax function, I have seen so far. Thank you very much Sir.

zeeshanahmed

Perfect! I recommend this channel to my class fellows! Thanks

mohammadalibalajlamresearc

God Bless my friend. Thank you for this simple tutorial.

nevin

very precise and to-the-point tutorial, thanks for the explanation :)

shivamsinghal

Thank you so much. This was very well explained!

alaishabarber

May Almighty Allah give u reward for helping students thanks dear sir

mohammadbilalniazi

Very sweet simple and to the point explanation..Normally I thums down the video if i dont like it...but your are amazing champ... Great delivery. Keep up the good work

anandgoel

Simple and Clear Explanation!! Thank You :)

vatsal_gamit

Thanks for the video...these simple concepts helps a lot in visualising a much bigger picture.

utkarshprakash

What happens under the hood when we run keras dense end layer, with softmax argument?

kurianbenoy

Cool! One question: why not just pick the value of the array that has the greatest magnitude?

AnsonSavage

Nicely described Bhavesh. Am learning Deep Learning from Coursera and was going through Logistics Regression, Sigmoid Gradient, Normalization. One of the steps was Softmax Function, was getting confused there. This helped me overcome that. Great work truely. Do you have a playlist that I could refer for Deep Learning Videos?

kurrysamir

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