Lecture

The Problem of Overfitting

In course

Laboris aute ad est commodo officia proident. Excepteur eiusmod ipsum anim incididunt nisi. Exercitation id minim tempor reprehenderit cillum magna qui laboris occaecat. Nulla aliqua voluptate duis exercitation.

Description

This lecture covers the problem of overfitting in deep networks, using examples from classification and curve fitting. It discusses the flexibility of deep networks, the classification of new inputs, and the detour into polynomial curve fitting. The instructor emphasizes the importance of controlling flexibility to avoid overfitting.

Instructor

magna nisi elit

Magna magna culpa elit nisi ex. Reprehenderit id pariatur cupidatat mollit cillum laborum sit commodo. Lorem ipsum amet do ullamco tempor sint qui nostrud in mollit ad id laboris sint. Ipsum consectetur magna nisi adipisicing ullamco dolor occaecat. Aliquip sunt qui est minim mollit duis qui tempor deserunt proident consequat labore qui.

Official source

https://mediaspace.epfl.ch/media/0_6qdgv80s

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

Information engineering

Machine learning: Artificial neural networks

Statistics

Data analysis: Cross-validation

Related lectures (32)

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.