<bos><start_of_turn>system
You are a helpful AI assistant.
lista de codigos linguisticos disponibles: ["es", "en"].<end_of_turn>
<start_of_turn>user
LSTM y unidades recurrentes cerradas<end_of_turn>
<start_of_turn>model
LSTM (Schmidhuber, 1997) and Gated Recurrent Units (GRUs) (Cho, 2014) are new 
variants of RNNs, have solved long-term dependency problems, and have attracted great 
attention. LSTMs were particularly developed to cope with the long-term dependency 
problem. The advantage of an LSTM model is that it uses the additional cell state, which 
is a horizontal sequence line on the top of the LSTM unit. This cell state is controlled 
by special purpose gates for forget, insert, or update operations It is able to decide the following:
• What kind of information we will store in the cell state
• Which information will be forgotten or deleted
In the original RNN, in order to learn the state of I tokens, it recurrently processes 
the entire state of previous tokens between timestep0
 and timestepi-1. Carrying entire 
information from earlier timesteps leads to vanishing gradient problems, which makes 
the model untrainable. The gate mechanism in LSTM allows the architecture to skip some 
unrelated tokens at a certain timestep or remember long-range states in order to learn the 
current token state. 
A GRU is similar to an LSTM in many ways, the main difference being that a GRU does 
not use the cell state. Rather, the architecture is simplified by transferring the functionality 
of the cell state to the hidden state, and it only includes two gates: an update gate and a 
reset gate. The update gate determines how much information from the previous and 
current timesteps will be pushed forward. This feature helps the model keep relevant 
information from the past, which minimizes the risk of a vanishing gradient problem as 
well. The reset gate detects the irrelevant data and makes the model forget it.
<end_of_turn><eos>