Neural networks are computer systems modeled after the human brain that can recognize patterns, make predictions, and improve over time by learning from data. They are a key part of machine learning and power technologies like facial recognition, voice assistants, spam filters, and real-time translation.

Neural networks belong to a broader category of artificial intelligence (AI), specifically a subset called machine learning (ML). Machine learning allows systems to learn from data without being explicitly programmed. Neural networks improve on traditional ML by handling more complex tasks — such as image recognition and speech understanding — with higher accuracy through a method known as deep learning, which uses multiple layers of virtual neurons to refine insights.

To train a neural network, engineers feed it large datasets and adjust it through repetition. The network gradually improves by tweaking the strength of the connections (called weights) between its artificial neurons to reduce errors — a process known as backpropagation. The result is a system that adapts and improves performance on tasks like classification, prediction, or decision-making.

How do neural networks work?

Neural networks process information by passing it through layers of interconnected units known as nodes or neurons. These are grouped into three primary types:

  • Input layer — takes in raw data, such as image pixels or spreadsheet values
  • Hidden layers — transform and interpret that data in stages
  • Output layer — produces the final result, such as a classification or recommendation

Each connection between neurons has a weight, which reflects its influence, and each node has a threshold. If a node’s input exceeds its threshold, it activates (or “fires”) and sends information to the next layer. This step-by-step process helps the network build up a structured understanding of the data — for example, detecting lines, shapes, and objects in an image of a cat.

How neural networks learn

Neural networks need to be trained before they can perform effectively. Training involves showing the model many examples and gradually teaching it to identify the correct patterns.

There are two main types of training data:

  • Labeled data — where each example includes a known outcome (e.g., an image labeled “dog”)
  • Unlabeled data — where the network identifies patterns on its own

Most business applications use supervised learning, where the model sees both the input and the correct output. It starts by making predictions, compares them with the correct answers, and adjusts its weights when it’s wrong. Over time, it gets better at making accurate predictions.

This iterative process enables neural networks to support applications like fraud detection in finance, automated transcription in customer support, or language translation in global communications.

Types of neural networks

While all neural networks share a common structure, different types are optimized for specific tasks.

Multi-Layer Perceptrons (MLPs)

An MLP is the most basic type of neural network, with fully connected layers linking inputs to outputs. MLPs are used for simple classification tasks like spam filtering, price prediction, or image recognition of handwritten digits.

Deep Neural Networks

DNNs contain multiple hidden layers, allowing them to model more complex relationships in data — a concept known as depth. Two important variants include:

  • Convolutional Neural Networks (CNNs): Ideal for image and video processing. CNNs detect features like edges, textures, and shapes, making them valuable in facial recognition, diagnostic imaging, and visual search tools.
  • Recurrent Neural Networks (RNNs): Best suited for sequential data. RNNs can “remember” past inputs, which makes them ideal for speech-to-text applications, real-time translation, and time-series forecasting.

These specialized architectures enable neural networks to adapt to a wide range of use cases, from pattern detection in medical diagnostics to sentiment analysis in customer feedback.

Neural networks use cases

Neural networks drive many AI-powered services that businesses and consumers rely on every day. Their ability to process large volumes of data and improve over time makes them especially valuable in enterprise environments.

Here are some of the main ways neural networks are being applied today:

  • Computer vision: Neural networks are the backbone of image recognition systems. They help self-driving cars detect road signs and pedestrians, enable photo apps to organise pictures by face or object, and assist doctors in identifying health issues in medical scans.
  • Speech recognition: Voice assistants like Siri, Alexa, and Google Assistant rely on neural networks to understand spoken commands. Businesses use similar systems in call centers to convert speech to text and direct customers more efficiently.
  • Natural language processing (NLP): Neural networks enable computers to read, interpret, and respond to human language. This powers everything from real-time translation tools and grammar checkers to advanced chatbots and email filters.
  • Recommendation engines: Platforms like Netflix, Spotify, and Amazon use neural networks to analyze user behavior and suggest what to watch, listen to, or buy next. These systems continuously learn and adapt to deliver increasingly relevant recommendations.

The neural network market is growing rapidly. Valued at $227.8 million in 2022, it’s expected to reach $1.4 billion by 2032, growing at 19.9% annually. Such expansion shows how businesses across many industries increasingly use neural networks for their operations.