Recurrent Neural Networks (RNN)

Recurrent Neural Networks (RNNs) are a class of artificial neural networks designed to handle sequences of data, making them particularly suited for tasks involving time series, natural language processing (NLP), speech recognition, and more. In contrast to conventional feedforward artificial neural networks, recurrent neural networks (RNNs) possess interconnections that form loops within themselves. This unique architecture empowers them to retain a recollection of preceding inputs, setting them apart from the more straightforward feedforward models. This architectural characteristic enables RNNs to capture temporal dependencies and relationships within sequential data.

The essence of RNNs lies in their ability to process sequences while considering the context of each element in relation to previous ones. This memory mechanism allows RNNs to model patterns that evolve over time, making them useful for tasks such as predicting the next word in a sentence, sentiment analysis, language translation, and even generating text. However, traditional RNNs suffer from vanishing and exploding gradient problems, limiting their ability to capture long-term dependencies effectively. To address these issues, more advanced RNN variants like Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) were developed.