Lecture

Interpreting Output as Probability

In course

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Description

This lecture explores the conditions necessary to interpret the output of a neural network as a probability, focusing on the cross-entropy error function for classification tasks. By ensuring a large dataset and a flexible network, it becomes possible to derive probabilities from the network's output.

Instructor

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Official source

https://mediaspace.epfl.ch/media/0_hb4gonjh

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Ontological neighbourhood

Information engineering

Machine learning: Artificial neural networks

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