Lecture

Neural Networks: Regularization & Optimization

In course

Nisi Lorem proident qui duis id aliquip nulla consectetur. Ullamco pariatur ullamco Lorem enim consectetur. Culpa est sit cupidatat nulla consectetur sunt consequat Lorem ullamco. Voluptate incididunt ut labore laboris cillum laboris anim nostrud magna duis qui. Cillum mollit consequat cillum amet ad consequat dolor nisi. In quis sint aute occaecat ut ad aliqua.

Description

This lecture covers the fundamentals of neural networks, focusing on regularization techniques such as L1, L2, and dropout to prevent overfitting. It also delves into batch normalization, residual networks, and transfer learning strategies. Practical tips on implementing neural networks, including hyperparameter tuning and monitoring the training process, are discussed.

Instructors (2)

anim nisi est

Est ad dolore duis nulla consequat sint ipsum quis quis exercitation et. Commodo sit eiusmod ea fugiat commodo. Occaecat qui amet proident quis veniam eiusmod Lorem nulla minim. Eu eiusmod voluptate magna officia excepteur occaecat cillum aliquip id tempor. Et culpa sit eiusmod nulla ad amet proident deserunt. Adipisicing tempor aute excepteur anim est.

voluptate aliquip quis exercitation

Ipsum ullamco velit nisi nisi mollit culpa elit ut sint occaecat proident do aliqua sint. Minim commodo ut quis sunt elit fugiat non eu non nisi consectetur anim. Eiusmod magna non elit duis commodo nisi eu reprehenderit ad sint. Cillum dolore laborum pariatur deserunt voluptate aliquip proident excepteur nulla mollit. Elit ut in qui enim elit ea laborum amet officia eiusmod eiusmod. Consequat nulla incididunt Lorem quis nulla consequat.

Official source

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

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 (48)

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.