Translations:Softmax Function/1/en: Difference between revisions

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    The '''softmax function''' (also called the '''normalized exponential function''') is a mathematical function that converts a vector of real numbers ('''logits''') into a probability distribution. It is the standard output activation for multi-class classification in neural networks and plays a central role in models ranging from logistic regression to large language models.
    The '''softmax function''' (also called the '''normalized exponential function''') is a mathematical function that converts a vector of real numbers ('''{{Term|logits}}''') into a probability distribution. It is the standard output {{Term|activation function|activation}} for multi-class classification in neural networks and plays a central role in models ranging from {{Term|logistic regression}} to large language models.

    Latest revision as of 23:34, 27 April 2026

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    Message definition (Softmax Function)
    The '''softmax function''' (also called the '''normalized exponential function''') is a mathematical function that converts a vector of real numbers ('''{{Term|logits}}''') into a probability distribution. It is the standard output {{Term|activation function|activation}} for multi-class classification in neural networks and plays a central role in models ranging from {{Term|logistic regression}} to large language models.

    The softmax function (also called the normalized exponential function) is a mathematical function that converts a vector of real numbers (logits) into a probability distribution. It is the standard output activation for multi-class classification in neural networks and plays a central role in models ranging from logistic regression to large language models.

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