Recurrent neural networks (RNNs) and their limitations

RNNs are a class of artificial neural networks that were designed to handle sequential data. They are particularly well-suited to tasks where the input data is temporally correlated or has a sequential nature, such as time series analysis, NLP, and speech recognition.

Overview of RNNs

Here are some essential aspects of how RNNs function:

• Sequence processing: Unlike feedforward neural networks, RNNs have loops in them, allowing information to persist. This is crucial for sequence processing, where the current output depends on both the current input and the previous inputs and outputs.
• Hidden states: RNNs maintain hidden states that capture temporal information. The hidden state is updated at each step of the input sequence, carrying forward information from previously seen elements in the sequence.
• Parameters sharing: RNNs share parameters across different parts of the model. This means that they apply the...