Lecture

Gradient Descent: Early Stopping and Stochastic Gradient Descent

In course

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Description

This lecture covers the implementation of gradient descent with early stopping and stochastic gradient descent. The instructor explains how to compute the gradient, update weights, and monitor training and validation losses to prevent overfitting. The differences between gradient descent, stochastic gradient descent, and mini-batch gradient descent are highlighted, showing how each method affects convergence speed and test error.

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Instructor

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Official source

https://mediaspace.epfl.ch/media/0_z59c2xip

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