RNN stands for Recurrent Neural Network. It is a type of artificial neural network designed to recognize patterns in sequences of data, such as text, genomes, handwriting, or spoken words.
Unlike Feedforward Neural Networks, RNNs can use their internal state (memory) to process sequences of inputs, making them extremely effective for tasks requiring memory or pattern recognition.
This makes RNNs particularly useful in applications like language modeling, translation, speech recognition, and time series prediction. However, standard RNNs can struggle with long sequences due to the vanishing or exploding gradients problem, which can be mitigated through variants like LSTMs (Long Short Term Memory) and GRUs (Gated Recurrent Units).
