The attention mechanism is a way to allow Long Short-Term Memory (LSTM) networks to selectively focus on specific parts of the input sequence when making a prediction.

At each time step, the LSTM network takes in the input sequence and uses its internal state to make a prediction. However, the standard LSTM model treats all the time steps in the input sequence equally, which can be suboptimal in some cases.

With the attention mechanism, the LSTM network can learn to selectively focus on certain parts of the input sequence more than others when making a prediction. It does this by learning to assign weights to different input values, which are then used to compute a weighted average of the input values.

The attention mechanism can be thought of as a way for the network to decide "where to look" in the input sequence to make its prediction. By focusing on the most relevant parts of the input sequence, it can make more accurate predictions and improve its overall performance.

The exact implementation of the attention mechanism can vary depending on the specific LSTM model and application. In general, it involves computing a set of attention weights for each time step in the input sequence and using these weights to compute a weighted sum of the input values, which is then passed through the LSTM network to make a prediction.
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