The softmax function, also known as softargmax or normalized exponential function, converts a vector of K real numbers into a probability distribution of K possible outcomes. It is a generalization of the logistic function to multiple dimensions, and used in multinomial logistic regression.
ELI5: imagine you’re in a class and the teacher asks a question. Several students raise their hands with different levels of confidence in their answers. The softmax function in AI is like a smart way of figuring out which student is most likely to be correct.
It looks at the confidence level of each student (which are like the values in AI data) and turns them into probabilities. The student with the highest confidence level gets the highest probability of being correct. This way, the softmax function helps the AI decide which option (or student, in our example) is most likely the right one, especially when it has to choose between many different options.
It’s used a lot in AI, particularly in tasks like recognizing objects in photos or understanding spoken words, where the AI has to pick the most likely option from many possibilities. In these cases, softmax helps the AI to be more confident about its choice by considering how likely each option is compared to the others.
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