Recurrent Neural networks
Recurrent Neural Networks (RNNs) Recurrent Neural Networks (RNNs) are a type of neural network designed to handle sequential data by leveraging their internal memory. Unlike traditional feedforward neural networks, RNNs can maintain information about previous inputs in their hidden states, making them suitable for tasks involving sequences, such as time series prediction, language modeling, and speech recognition. Key Concepts Sequential Data : RNNs are designed to handle sequences of data, such as sentences, time series, and audio signals. They can process variable-length sequences and maintain context from previous inputs. Recurrent Connections : In RNNs, the output from one time step is fed back into the network as input for the next time step. This recurrence allows the network to maintain a hidden state that captures information about previous inputs. Hidden State : The hidden state is a dynamic representation of the input sequence up to the current time step. It is updated...