RNN (Recurrent Neural Network)

Information

• RNN (Recurrent Neural Network) is a type of neural network designed for processing sequential data, such as time series or natural language.
• It can handle input of varying length, and maintain an internal state (memory) that allows it to capture information from previous inputs.
• RNNs can be trained with backpropagation through time (BPTT) to optimize their weights and learn to predict future values based on past values.
• A basic RNN cell is composed of a simple layer with an activation function, which takes in the input and the previous hidden state as inputs, and produces the output and the next hidden state as outputs.
• The most common type of RNN is the LSTM (Long Short-Term Memory), which uses special memory units to allow for longer-term dependencies and better gradient flow during training.
• Another common variant is the GRU (Gated Recurrent Unit), which combines the forget and input gates of the LSTM into a single "update gate" for a simpler architecture.
• RNNs can be used for a variety of tasks, including sequence prediction, sequence generation, and sequence classification.
• However, they can suffer from vanishing or exploding gradients due to the long-term dependencies and recurrent weight updates, which can make training difficult.
• Various techniques such as gradient clipping, weight regularization, and attention mechanisms can be used to address these issues and improve RNN performance.