Lecture

Recurrent Neural Networks: LSTMs & GRUs

Description

This lecture covers the continuation of recurrent neural networks with multiple layers and bidirectionality, focusing on LSTM and GRU variants. It discusses the challenges of learning long-range dependencies, the use of gates in GRUs, and the advantages of LSTMs and GRUs over traditional RNNs. The instructor explains the concepts through practical examples and addresses issues like vanishing gradients and optimal architectures for different tasks.

Instructor

officia pariatur non labore

Aute exercitation tempor labore eiusmod nulla veniam ullamco mollit veniam amet ullamco. Nulla qui eiusmod laborum ullamco dolor elit et. Excepteur magna esse enim dolor. Culpa reprehenderit officia do laborum minim exercitation esse labore magna elit aliquip in. Culpa magna incididunt amet enim culpa aliquip sit nulla.

Official source

https://mediaspace.epfl.ch/media/0_y1sbestm

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

Related lectures (36)

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.