Hands-On Transfer Learning with Python

Recurrent neural networks

An recurrent neural Network (RNN) is specialized for processing a sequence of values, as in x(1), . . . , x(t). We need to do sequence modeling if, say, we wanted to predict the next term in the sequence given the recent history of the sequence, or maybe translate a sequence of words in one language to another language. RNNs are distinguished from feedforward networks by the presence of a feedback loop in their architecture. It is often said that RNNs have memory. The sequential information is preserved in the RNNs hidden state. So, the hidden layer in the RNN is the memory of the network. In theory, RNNs can make use of information in arbitrarily long sequences, but in practice they are limited to looking back ...