How deeply does Deep Learning with Python, Third Edition cover single-label categorical classification?

Specifies output width, activation, and target-dependent loss, but stops short of explaining training or probability computation.

Single-label categorical classification

As explained in Deep Learning with Python, Third Edition

A classification task in which each example belongs to exactly one class from a set of mutually exclusive classes.

Single-label categorical classification predicts one class per example. A neural network uses one output unit per class, with softmax producing the class probabilities. section 20.1.6

Keras output layer

For one-hot encoded targets, finish a dense network with Dense(num_classes, activation="softmax") and compile it with categorical_crossentropy. Integer targets instead use sparse_categorical_crossentropy. section 20.1.6

How Deep Learning with Python, Third Edition covers it

Cover of Deep Learning with Python, Third Edition

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Building LLM Applications with DSPyA single-label problem is a classification setup in which each input is assigned at most one class label.

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Related concepts

multilabel classificationcontrasts with · Multilabel classification is a classification task in which each input may be assigned zero or more labels simultaneously.
classificationkind of · Classification is the task of assigning an input or observation to one or more categories from a defined set of labels.

This explainer was generated by Manning's knowledge graph from the text of Deep Learning with Python, Third Edition. The section links point to the parts of the book it draws on.