Chapter 9: Recurrent Neural Networks (RNNs) in PyTorch
Abstract: Recurrent Neural Networks (RNNs) are a class of neural networks designed to process sequential data by maintaining a hidden state that captures information from previous inputs. PyTorch provides a convenient nn.RNN module for implementing RNNs. Key Concepts: Sequential Data Processing: RNNs excel at tasks involving sequences, such as natural language processing (NLP), speech recognition, and time series prediction, where the order of data points matters. Hidden State: Unlike traditional feedforward networks, RNNs have a recurrent connection that feeds the hidden state from the previous time step as an input to the current time step. This allows the network to "remember" past information. Unrolling Through Time: An RNN can be visualized as a series of identical network units, one for each time step in the sequence. Each unit receives the current input and the hidden state from the previous unit, producing an output and...