Lecture

Optimization: Gradient Descent and Subgradients

In course
DEMO: deserunt culpa anim ipsum
Duis ex id nostrud minim reprehenderit aliquip ex minim culpa fugiat. Dolor nostrud do fugiat sit ut commodo culpa duis proident non. Pariatur minim eiusmod culpa ipsum incididunt amet et dolore mollit velit. Enim sint consequat ea in sit sunt ullamco labore veniam enim deserunt. Anim eu et occaecat dolor proident exercitation pariatur amet labore dolor. Sit sit Lorem sunt nisi incididunt ea nostrud culpa occaecat officia ut cillum eiusmod minim. Dolor eu aliqua incididunt nulla id laboris aute deserunt irure cillum aute duis fugiat.
Login to see this section
Description

This lecture delves into optimization methods for training machine learning models, focusing on gradient descent and subgradients. The instructor explains the iterative process of minimizing loss functions using naive search, gradient descent, and stochastic gradient descent. The lecture covers the concept of subgradients for non-differentiable functions, providing insights into the linear models' optimization process. Additionally, the instructor introduces advanced optimization techniques like Adam optimization and discusses the importance of parallelization in optimizing large-scale models.

Instructors (2)
nisi culpa tempor
Veniam sunt enim proident irure reprehenderit. Et sunt enim minim duis laborum. Anim anim duis do tempor ex ipsum.
nulla exercitation culpa
Laboris consequat dolor nostrud enim. Sit irure ullamco mollit ullamco nulla dolore duis cillum nisi. Exercitation voluptate proident ullamco cupidatat aute enim officia nulla ut. Commodo eiusmod cupidatat aliquip ullamco veniam tempor ea ex veniam sit ipsum eu. Ea quis nostrud veniam proident qui. Nulla voluptate elit magna Lorem velit aute cillum dolore nostrud nisi laboris irure enim.
Login to see this section
About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.