The attention mechanism in LSTM networks allows the network to selectively focus on certain parts of the input sequence, enabling it to capture long-term dependencies more effectively.

Here's a high-level overview of how the attention mechanism works for LSTM networks:

The LSTM network processes the input sequence, generating a set of hidden states that represent the sequence.

The attention mechanism takes the hidden states and computes a set of attention weights for each time step in the sequence. These weights indicate how important each time step is for the final output.

The attention weights are then used to compute a weighted sum of the hidden states, with the weights acting as coefficients. This produces a context vector, which is a summary of the input sequence that takes into account the importance of each time step.

The context vector is concatenated with the LSTM output at the current time step and fed through a fully connected layer to produce the final output.

The attention mechanism allows the LSTM network to selectively focus on parts of the input sequence that are most relevant for the task at hand. This can improve the network's ability to capture long-term dependencies and make more accurate predictions. Additionally, the attention mechanism can also provide interpretability, allowing us to understand which parts of the input sequence the network is focusing on.
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