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Lecture
Are you an EPFL student looking for a semester project?
Work with us on data science and visualisation projects, and deploy your project as an app on top of Graph Search.
This lecture covers the variants of gradient descent used in practice, focusing on stochastic gradient descent (SGD) and its properties. It explains how SGD works by choosing a minibatch of samples at every iteration, approximating the full derivative. The lecture also delves into linear classification, discussing the concept of a linear separator and the importance of having differential properties almost everywhere.