In my work with Recurrent Neural Networks (RNNs), I quickly ran into their biggest limitation: the vanishing gradient problem. This makes it very difficult for a simple RNN to learn patterns and dependencies that span long sequences. Thankfully, a more advanced recurrent architecture was developed to solve this exact problem: the Long Short-Term Memory (LSTM) network.
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LSTMs are a special kind of RNN that are explicitly designed to remember information for long periods. They achieve this through a more complex internal structure called a ‘cell’. I’ve found them to be incredibly effective for a wide range of tasks involving sequential data. This guide will explain the key components of an LSTM cell.
🧠 The LSTM Cell and its Gates
The key innovation of the LSTM is its cell state and a series of ‘gates’. The cell state acts as a sort of conveyor belt, allowing information to flow down the entire sequence with minimal changes. The gates are neural network layers that regulate which information is allowed to be added to or removed from this cell state.
I think of these gates as a series of sophisticated valves that control the flow of information. There are three main gates in a standard LSTM cell:
- The Forget Gate: This gate decides what information should be thrown away from the cell state. It looks at the previous hidden state and the current input and outputs a number between 0 and 1 for each piece of information in the cell state. A 1 means ‘keep this completely,’ while a 0 means ‘forget this completely.’
- The Input Gate: This gate decides which new information we’re going to store in the cell state. It has two parts: a sigmoid layer that decides which values to update, and a tanh layer that creates a vector of new candidate values to be added.
- The Output Gate: This gate decides what the next hidden state should be. It takes the (now updated) cell state, filters it, and combines it with the current input to produce the output for the current time step.
✅ How LSTMs Solve the Vanishing Gradient Problem
The gating mechanism is what allows LSTMs to overcome the vanishing gradient problem. The cell state acts as a separate, more stable pathway for gradients to flow through during backpropagation. The forget gate gives the network the ability to learn to ‘reset’ its memory when it encounters the start of a new, important piece of information.
This structure allows the network to maintain a constant error flow and learn the long-range dependencies that are impossible for a simple RNN to capture. It’s this ability that has made LSTMs the go-to architecture for many complex sequence modeling tasks, from machine translation to speech synthesis.
- A Practical Guide to Overfitting and Regularization in Deep Learning
- A Guide to Generative Adversarial Networks (GANs)
- A Guide to Autoencoders for Dimensionality Reduction
- A Guide to Recurrent Neural Networks (RNNs) for Sequential Data
- A Guide to Convolutional Neural Networks (CNNs) for Image Recognition
- A Guide to Backpropagation – How Neural Networks *Really* Learn
- How Neural Networks Learn – A Guide to Loss Functions and Optimization
