Softmax

As explained in Sutskever's List

A function that converts a vector of scores into nonnegative values summing to one, interpreted as probabilities.

How Sutskever's List covers it

How deep it goes

Defines the score-to-probability transformation and shows its role in classification without detailing its computation.

Examples

Uses AlexNet’s 1,000 ImageNet category scores as the concrete classification example.

What you can do after

Understand how raw category scores become predicted probabilities and determine a classification.

How it connects

Connects softmax to fully connected layers, AlexNet, and ImageNet classification.

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Also explained in

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  Optimization Algorithms: AI techniques for design, planning, and control problemsSoftmax is a function that converts a vector of real-valued scores into nonnegative class probabilities that sum to one.
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  Server-Side WebAssemblySoftmax is a mathematical transformation that converts a vector of scores into nonnegative probabilities summing to one.
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  Deep Learning with PyTorch, Second EditionA normalization function that converts a vector of scores into a probability distribution by exponentiating and renormalizing the values.
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  Build a Reasoning Model (From Scratch)A normalization function that converts logits into a probability distribution over possible next tokens.
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  Math and Architectures of Deep LearningA smooth function that converts an unnormalized score vector into a probability-like distribution over classes.
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  Grokking Machine LearningA function that converts multiple real-valued scores into positive values whose normalized values sum to one and represent class probabilities.
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  Learn Generative AI with PyTorchAn activation that converts raw scores into a probability distribution over multiple classes or positions.
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  Transformers in ActionThe softmax function converts a vector of scores into nonnegative values that sum to one.
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  Build a Text-to-Image Generator (from Scratch)A function that converts a vector of real-valued scores into nonnegative values that sum to one.
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  Natural Language Processing in Action, Second EditionSoftmax is a normalized exponential function that converts a vector of scores into nonnegative values summing to one.
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  Inside Deep Learning: Math, Algorithms, ModelsA function that turns a vector of scores into normalized weights over items or classes by exponentiating and renormalizing them.
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  Deep Learning with R, Third EditionA softmax operation converts a vector of scores into nonnegative values that sum to one, typically representing class probabilities.
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  Hugging Face in ActionSoftmax is a mathematical function that converts a vector of numbers into a probability distribution by exponentiating and normalizing its values.
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  Machine Learning Algorithms in DepthA function that converts a vector of scores into nonnegative values summing to one, typically interpreted as class probabilities.
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  Data Without LabelsA softmax function converts multiple real-valued outputs into nonnegative values that sum to one, typically representing class probabilities.
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  Grokking AI Algorithms, Second EditionThe softmax function converts a vector of real-valued scores into nonnegative values that sum to one.

Compare how 17 books explain softmax →

Related concepts

• probability distributionproduces · A probability distribution is a mathematical specification assigning probabilities to the possible values or outcomes of a random variable, with total probability one.
• temperatureused by · A temperature parameter adjusts the concentration of a probability distribution, affecting sampling randomness or the likelihood of accepting less favorable transitions.
• reduction operationuses · A reduction operation combines multiple input values or partial results according to a combining rule to produce fewer results, often a single aggregate.
• probability theoryproduces · Probability theory is the branch of mathematics that formalizes uncertainty and analyzes random events and outcomes using probability measures.
• attentionused by · Attention is a neural-network mechanism that computes input-dependent weights to combine information according to its relevance to a computation.
• attention weightproduces · An attention weight is a computed coefficient, often normalized, that determines how strongly an input element or value contributes to an attention-based representation.

This explainer was generated by Manning's knowledge graph from the text of Sutskever's List. The section links point to the parts of the book it draws on.