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

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Official source

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

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Ontological neighbourhood

Information engineering

Machine learning: Artificial neural networks

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