We previously examined the difficulties in training simple Recurrent Neural Networks, particularly the vanishing and exploding gradient problems. These issues make it hard for basic RNNs to capture dependencies between elements that are far apart in a sequence.

This chapter introduces Long Short-Term Memory (LSTM) networks, a specialized RNN architecture specifically designed to overcome these limitations. We will look at the core components that allow LSTMs to selectively remember or forget information over long sequences.

You will learn about:

• The concept of gating mechanisms that regulate information flow within the network.
• The detailed architecture of an LSTM cell, including the forget gate, input gate, and output gate.
• How the cell state acts as a conveyor belt for information, allowing it to pass through the network with minimal degradation.
• The mathematical operations ($\sigma$, $\tanh$) governing the updates within an LSTM cell.
• Why LSTMs are generally more effective than simple RNNs for tasks requiring the modeling of long-range dependencies.

By the end of this chapter, you will understand the internal workings of LSTM cells and appreciate their significance in modern sequence modeling.