What is LSTM - Long Short-Term Memory?

Technological advances never rest. How many exist that you probably don’t even know about, yet benefit from every day—especially if you work in the IT world? One of these is long short-term memory (LSTM). The LSTM model is a method for training neural networks and storing important long-term information. It plays a crucial role in the development of artificial intelligence.

What are long and short-term memory LSTM?

Long-short-term memory (LSM) is a computing technique for storing information within a neural network over an extended period of time. It is widely used in deep learning.

This model is ideal for sequence prediction tasks, allowing the network to access past events and consider them for new calculations. This feature sets it apart from standard recurrent neural networks (RNN), which use a single hidden state passed through time.

LSTM architecture: what elements it includes

LSTMs introduce a new component called the memory cell, which allows the network to store and retrieve information over long durations. A cell consists of elements that give the network several options. It must be capable of storing information for extended periods and linking it with new input when needed.

But that’s not all. It’s also essential that the cell automatically removes outdated or irrelevant knowledge from its “memory.” That’s why each cell is controlled by three gates:

• Input gate: controls what information is added to the memory cell.

• Forget gate: decides what information should be deleted from the cell.

• Output gate: controls what information leaves the cell.

How does long short-term memory work?

Long short-term memory cells operate in layers. However, unlike other networks, they retain information over long periods and can process or retrieve it later. Each LSTM cell uses the three gates, as well as a form of short-term and long-term memory.

In the context of long short-term memory, short-term memory is called the hidden state. But unlike other networks, an LSTM cell can also retain long-term memory, which is stored in what’s known as the cell state. New information flows through the three gates.

In the input gate, the current input is multiplied by the hidden state and the previous cycle’s weights. This determines the value of the new input. Important information is then added to the current cell state, becoming the new updated state.

The forget gate decides which information is kept and which is discarded. It considers both the previous hidden state and the current input. A sigmoid function generates values between 0 and 1 to make this decision—0 means the previous information is forgotten, and 1 is kept as part of the cell’s state.

The final result is calculated through the output gate. This gate uses the hidden state and a sigmoid function then applies a tanh (hyperbolic tangent) activation to the cell state. Then multiply the result to decide what information should exit the cell.

How can LSTM help a software developer?

Now you may be wondering how LSTM can help you in your projects. The relationship between LSTM and software developers depends on the kind of software being built. While LSTM is a machine learning technique, its usefulness grows when working with sequential data, real-time inputs, text, or applied AI. Many mobile application developers use LSTM to enhance real-time data processing in intelligent apps. Some concrete examples include:

Natural Language Processing (NLP)

Common cases: chatbots, grammar correctors, smart search engines.

Example: An LSTM can predict the next word in a sentence or analyze the sentiment of a review.

Time series modeling

Common cases: financial software, IoT, demand forecasting, predictive maintenance.

Example: Predicting future sales values or temperature patterns.

Speech and audio recognition

Common cases: virtual assistants, and audio transcription software.

Example: Converting voice to text by analyzing sound wave sequences.

Anomaly detection

Common cases: cybersecurity, server monitoring, fraud detection.

Example: Detecting unusual server behavior based on sequential log data.

So, when should professional or beginner developers be interested in this? Whenever the software needs to learn from patterns in data that have temporal or contextual order. Also, there's no need to build LSTMs from scratch—libraries like TensorFlow, Keras, and PyTorch simplify the process significantly.

Powerful, but not perfect

However, despite their power and flexibility, LSTM networks also come with challenges. They’re more complex to train than traditional neural networks due to their greater number of parameters. They also require more training data and computing power to produce optimal results. LSTMs can be prone to overfitting if the data isn’t diverse or representative enough.

Even with these drawbacks, LSTM networks continue to attract great interest in the deep learning community. Ongoing research aims to improve their efficiency and performance. Variants like bidirectional LSTMs, stacked LSTMs, and attention-based LSTMs are emerging to meet specific needs and deliver better results.

LSTM: A key advancement for your next software project

LSTM networks have revolutionized the field of deep learning. Their ability to retain and use information across long periods opens new perspectives in many areas of application. Machine translation, voice recognition, sequence analysis, or other tasks, LSTMs offer a powerful and promising approach for modeling complex data and capturing temporal patterns.

As research continues, LSTM networks are likely to keep evolving and play a vital role in the future of AI and machine learning. These advances will unlock new opportunities for software developers, offering them a tool that can enhance and accelerate the creation of smarter applications.

LSTM works—and some of the world’s biggest companies prove it. Google, for instance, uses it for its smart assistance systems, Google Translate, AlphaGo, and voice recognition on smartphones. On the other hand, Apple and Amazon rely on long short-term memory for their virtual assistants, Siri and Alexa. Apple also benefits from LSTM in its keyboard’s autocomplete function.