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## What exactly is attention in a RNN?
In a Recurrent Neural Network (RNN), attention is a mechanism that allows the model to focus on certain parts of the input while processing it, rather than considering the entire input equally. This can be particularly useful when working with long sequences, as it allows the model to selectively pay attention to the most relevant parts of the input, rather than being overwhelmed by the sheer length of the sequence.

## How to implement attention?
There are several ways to implement attention in an RNN, but one common approach is to use an attention layer that is trained to learn the importance of different parts of the input sequence. This layer takes in the output of the RNN at each time step, as well as the hidden state of the RNN, and produces a weighted sum of the output at each time step, where the weights reflect the importance of each time step. 

The attention layer can hereby be implemented using a variety of techniques, such as using a dot product or a multi-layer perceptron to compute the weights for each time step. In some cases, it may also be useful to incorporate additional information, such as the context vector - a summary of the input sequence up to that point - in order to improve the accuracy of the attention weights.

## How to use attention weights?
Once the attention weights have been calculated, they can be used to produce a weighted version of the input sequence, which is then used as input to the next layer of the network. This allows the network to selectively focus on the most relevant parts of the input sequence while processing it, rather than considering the entire sequence equally.

## Where to use this in practice?
One common application of attention in RNNs is in natural language processing tasks, such as machine translation or text summarization. In these tasks, the input sequences are typically long sequences of words, and the attention mechanism allows the model to focus on the most relevant parts of the input when generating the output.

For example, in machine translation, the attention mechanism can be used to focus on the words in the source language sentence that are most relevant for generating the corresponding translation in the target language. This can be particularly useful when translating idiomatic expressions or rare words, as it allows the model to focus on the specific words that are most important for generating the correct translation.

In addition to natural language processing tasks, attention mechanisms in RNNs have also been applied to a wide range of other tasks, including image captioning, speech recognition, and even video game AI. In these tasks, the attention mechanism can be used to selectively focus on the most relevant parts of the input, such as specific objects or spoken words, in order to improve the performance of the model.

## Summary

Overall, attention mechanisms can be a useful tool for improving the performance of RNNs on tasks involving long sequences, as they allow the network to selectively focus on the most relevant parts of the input, rather than being overwhelmed by the length of the sequence.