Recurrent neural networks (rnn) and long short term memory networks (lstm)

The document appears to describe the architecture and processes involved in training a recurrent neural network (RNN) using input sequences and specified weight matrices. It outlines the computation of hidden state vectors and outputs through the use of activation functions like tanh and updates to weights over multiple time steps. Additionally, it includes equations for calculating loss and adjustments to the model's parameters during training.