[Artificial Intelligence] Basic introduction to Softmax function, application scenarios, advantages and disadvantages, and code implementation
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Basic introduction to Softmax function, application scenarios, advantages and disadvantages, code implementation, markdown format, latex formula, 10,000 words.
Softmax function introduction
In machine learning, the softmax function is an activation function used for polynomial classification problems. It converts a K-dimensional vector into K probability distributions ranging from [0,1] and summing to 1. It is often used to map the output of the last layer to a probability distribution, allowing the classifier to predict the probability of each class.
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The formula of the Softmax function is as follows: