How deeply does Data Without Labels cover softmax?

Defines probability output and prediction selection, but does not detail the mathematical formula or implementation procedure.

Softmax

As explained in Data Without Labels

A softmax function converts multiple real-valued outputs into nonnegative values that sum to one, typically representing class probabilities.

Softmax converts a neural network’s final outputs into probabilities for multiple classes. These nonnegative values sum to one, making the largest value the predicted class. section 8.2.4 section 8.6.1

Use softmax when:

A neural network performs multiclass classification. section 8.2.4
The final layer must express competing class predictions as probabilities. section 8.2.4 section 8.6.1
A convolutional neural network passes extracted image features through a fully connected layer to its classification output. section 8.6.1

Image classification

For an image classified as a car, bike, or truck, softmax produces one probability for each category; the highest probability determines the prediction. The same pattern applies to cat-versus-dog classification. section 8.2.4 section 8.6.1

How Data Without Labels covers it

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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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Sutskever's ListA function that converts a vector of scores into nonnegative values summing to one, interpreted as 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.

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This explainer was generated by Manning's knowledge graph from the text of Data Without Labels. The section links point to the parts of the book it draws on.