What is RNN / Recurrent Neural Network

RNN, or Recurrent Neural Network, is a type of deep learning model particularly suited for processing sequential data such as time series or natural language. Unlike traditional neural networks where inputs and outputs are static, RNNs can handle dynamic sequences, as they have a 'memory' feature that allows them to retain information from previous inputs for use in subsequent outputs.

This capability makes RNNs excel in fields such as natural language processing (NLP), speech recognition, and video analysis. The core of RNN lies in its recurrent structure, which captures dependencies in time series data by passing information through cycles.

However, RNNs also come with drawbacks, most notably the vanishing and exploding gradient problems, which hinder performance on long sequences. To address these issues, researchers introduced more complex variants like Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRU), which perform better on many tasks.

As deep learning evolves rapidly, the applications of RNNs continue to expand, including machine translation, sentiment analysis, and generative models. In the future, RNNs and their variants will keep driving advancements in artificial intelligence, especially in areas requiring sequential data processing.