Long Short-Term Memory (LSTM) Networks

In this chapter, we dive into Long Short-Term Memory (LSTM) networks, a type of RNN architecture specifically designed to address the issues of vanishing gradients and long-term dependencies. LSTMs are capable of remembering information for long periods, which makes them particularly useful for tasks involving sequences.

• LSTM Structure: LSTMs consist of three main components—forget gate, input gate, and output gate. These gates control the flow of information in the network, allowing it to decide what to remember and what to forget;
• Forget Gate: the forget gate determines what information from the previous time step should be discarded. It outputs a number between 0 and 1, where 0 means "forget" and 1 means "retain" the information;
• Input Gate: the input gate controls what new information will be added to the cell state. It also outputs a value between 0 and 1, deciding how much of the new data should be incorporated;
• Output Gate: the output gate decides which part of the cell state will be outputted. The cell state is updated at each time step based on the interactions between these gates;
• Advantages of LSTMs: LSTMs are better at handling long-term dependencies compared to traditional RNNs. The gates in an LSTM help prevent the vanishing gradient problem, making it possible for the network to learn and remember information over many time steps.

In summary, LSTMs are a powerful extension of RNNs that address key limitations of traditional RNNs, particularly when dealing with long sequences or tasks that require remembering information over time.