Deep Learning

RNN

Processes sequences one step at a time, carrying a hidden state forward as memory.

A recurrent network feeds its own output back as input at the next step, so the hidden state is a running summary of everything seen so far.

In practice gradients vanish across long sequences and the strictly sequential processing prevents parallel training — the two problems that gated cells and then transformers were built to solve.

Also in Deep Learning

CNN LSTM Transformers Auto Encoders Deep RL Epochs

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