Introducing LSTM
RNNs are widely used for sequence modeling tasks, but they suffer from limitations in capturing long-term dependencies in the data. An advanced version of RNNs, known as LSTM, was developed to address these limitations. Unlike simple RNNs, LSTMs have a more complex mechanism to manage context, enabling them to better capture patterns in sequences.
In the previous section, we discussed GRUs, where hidden state 
is used to carry the context from timestep to timestep. LSTM has a much more complex mechanism for managing the context. It has two variables that carry the context from timestep to timestep: the cell state and the hidden state. They are explained as follows:
- The cell state (represented as
): This is responsible for maintaining the long-term dependencies of the input data. It is passed from one timestep to the next and is used to maintain information across a longer period. As we will learn later in this section, it is carefully determined by...
