Recurrent Neural Network

Recurrent Neural Networks (RNNs) are a type of neural network designed for processing sequential data.

RNNs are used in applications where the order of the data is important, such as time series prediction, natural language processing, and speech recognition. They work by maintaining a hidden state that captures information about previous elements in the sequence, allowing them to learn temporal dependencies.

An RNN processes an input sequence one element at a time, updating its hidden state at each step. The hidden state is then used to make predictions or generate outputs. This allows RNNs to handle variable-length sequences and capture patterns over time.

For example, in language modeling, an RNN can predict the next word in a sentence by considering the previous words. As it processes each word, it updates its hidden state to reflect the context provided by the preceding words. This enables the RNN to generate coherent and contextually relevant text.

RNNs are important because they provide a way to model sequential data, which is common in many real-world applications. They are foundational to more advanced architectures like Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), which address some of the limitations of basic RNNs, such as difficulty in learning long-term dependencies.

Attention mechanisms try to solve a similiar problem. Unlike RNNs, they allow the model to focus on different parts of the input sequence instead of keeping an hidden state. This can lead to better performance on tasks involving long-range dependencies. Attention mechanisms are a key component of Transformer models, which have largely replaced RNNs in many natural language processing tasks.