Softmax is a mathematical function that converts a vector of real numbers into a probability distribution. It takes each element in the vector, exponentiates it, and normalizes by dividing by the sum of all exponentiated values, ensuring the output probabilities sum to one. This is particularly important in the context of neural networks where softmax is commonly used in the output layer to represent class probabilities for classification tasks.
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