Gradient Descent: Optimization and Regularization

In course

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Description

This lecture covers the algorithm of gradient descent for optimization, including early stopping and regularization techniques. It explains the process of finding the minimum of a function using iterative updates and implicit regularization. Examples and mathematical derivations are provided to illustrate the concepts.

Instructor

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Official source

https://mediaspace.epfl.ch/media/0_24nn5ll7

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Ontological neighbourhood

Mathematics

Analysis: Complex analysis

Number theory: Number theory

Information engineering

Machine learning: Artificial neural networks

Statistics

Data analysis: Cross-validation

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