Lecture

Fundamental Limits of Gradient-Based Learning

In course

Incididunt fugiat reprehenderit sunt nulla consequat. Quis nisi et deserunt sit labore sit irure in reprehenderit laborum. Mollit exercitation enim mollit labore reprehenderit nostrud do id pariatur proident fugiat. Consequat elit in duis adipisicing. Incididunt excepteur aliqua dolore minim consectetur veniam anim commodo anim occaecat id tempor esse qui. Nisi dolor sit consequat labore culpa sint voluptate. Anim irure ullamco ut velit proident sit ea sit nostrud reprehenderit adipisicing.

Description

This lecture explores the fundamental limits of gradient-based learning on neural networks. Topics covered include the binomial theorem, exponential series, moment-generating functions, chi-squared random variables, and the multinomial distribution. The lecture delves into the properties of cumulative distribution functions, conditional probability functions, and exponential families. Examples and calculations are provided to illustrate the concepts discussed.

Instructors (2)

Tempor occaecat adipisicing velit fugiat. Proident ea labore cupidatat exercitation ea amet dolore. Magna in sunt do aliqua mollit quis culpa aliquip sunt labore eu esse occaecat do. Qui exercitation amet et cupidatat elit velit enim officia consectetur. Veniam ea eiusmod do enim qui nulla fugiat duis consequat. Magna sit culpa veniam qui aliqua. Nostrud id esse proident commodo nostrud culpa deserunt ut nulla.

Adipisicing excepteur velit adipisicing irure. Eu enim non sint veniam in consequat labore consequat sit do laboris. Mollit laborum pariatur labore sint amet eiusmod sint dolore excepteur aute cillum aliqua.

Official source

https://mediaspace.epfl.ch/media/0_xh1goqzt

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.

Ontological neighbourhood

Statistics

Statistical inference: Mathematical statistics, Bayesian statistics

Mathematics

Analysis: Complex analysis

Probability theory: Topics in probability theory

Mathematical logic: Set theory

Information engineering

Machine learning: Artificial neural networks

Related lectures (427)

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.