LSTMs & GRUs

Summary

Gated variants of RNNs that use memory cells and gates to retain information over longer sequences — solved the vanishing gradient problem that limited plain RNNs.

How to Learn This

1Train an LSTM on a sequence task and compare it to a plain RNN.
2Learn what the forget, input and output gates each control.
3Understand why GRUs are a simpler, often faster alternative to LSTMs.

Recurrent Neural Networks (RNNs)

Sequence-to-Sequence Models

More topics in Deep Learning Architectures

Convolutional Neural Networks (CNNs)Pooling Layers Recurrent Neural Networks (RNNs)Sequence-to-Sequence Models Attention Mechanism Transformer Architecture Positional Encoding Transfer Learning Autoencoders Generative Adversarial Networks (GANs, Overview)

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