Regularized Cross-Entropy Risk

In course

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Description

This lecture covers the regularized cross-entropy risk in neural networks. It explains the training process of feedforward neural networks by minimizing the cross-entropy empirical risk. The lecture delves into the motivation behind cross-entropy minimization, sensitivity factors, and the challenges faced in deep networks. Practical examples and simulations are provided to illustrate the concepts discussed.

Instructor

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Official source

https://mediaspace.epfl.ch/media/0_ogk5bjo1

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Ontological neighbourhood

Information engineering

Machine learning: Artificial neural networks, Topics in machine learning

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