Lecture

Careful Cross-validation

In course

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Description

This lecture covers the importance of careful cross-validation in deep neural networks, including the split of data into training, validation, and test sets, as well as the concept of K-fold cross-validation. The instructor explains the process of splitting the data and the significance of never touching the test data until the final evaluation. Examples using the MNIST dataset are provided to illustrate the practical implementation of careful cross-validation.

Instructor

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Official source

https://mediaspace.epfl.ch/media/0_4xaenbq1

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Ontological neighbourhood

Information engineering

Machine learning: Artificial neural networks

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