Neural network
A model inspired by the brain
A neural network is a computing model loosely inspired by how the brain works. It consists of connected nodes (neurons) arranged in layers that learn to recognize patterns in data. It is the foundation of modern machine learning and powers technologies like image and speech recognition and the language models behind tools such as ChatGPT.
How a neural network works
The network is made of artificial neurons arranged in layers. The input layer takes in data — the pixels of an image, for example — which pass through hidden layers, and the output layer returns a result, say “there is a cat in the picture.” Every connection between neurons has a weight that determines how strongly a given signal influences the outcome.
The network learns from examples. During training you show it many samples together with the correct answers, and after each mistake it gradually adjusts the weights so it errs as little as possible. When a network has many layers, we speak of deep learning — and that is what powers today’s most capable models.
Where neural networks are used
Neural networks are suited to tasks that are hard to describe with fixed rules but for which enough examples can be gathered. They learn patterns from data that a programmer would struggle to spell out by hand, which is why they power most of what we now perceive as “intelligent” features.
- Image recognition: sorting photos, quality control, object detection.
- Language processing: translators, chatbots, and large language models.
- Recognizing and generating speech and audio.
- Recommendations, demand forecasting, and fraud detection in data.
What a neural network means for a business
For a business the math behind the network is not what matters — what matters is that it can solve tasks based on patterns in data: estimating, sorting, or recognizing. The quality of the result, however, depends fundamentally on the amount and quality of the data the network learns from.
A second limit is explainability. A neural network often works like a “black box” — it gives you an answer but can hardly justify exactly why. For sensitive decisions this needs to be taken into account, and the model’s outputs should be reviewed sensibly.
Considering a solution built on a neural network?
Explore our software developmentFrequently asked questions
Is a neural network the same as artificial intelligence?
Not quite. A neural network is one of the approaches used to build AI — the most successful one today. Artificial intelligence is a broader term that also includes other methods, but most modern AI systems are built precisely on neural networks.
How much data does a neural network need?
Usually quite a lot, and high-quality above all — from scarce or biased data it learns the wrong patterns. For specific tasks you can start from already pre-trained models that only need fine-tuning on a smaller amount of your data.
Is a neural network worth it for a smaller company too?
Yes, you no longer have to train one from scratch. Thanks to ready-made models and cloud services you can tap its power even in a smaller project — the key is to pick a task where it genuinely saves time or adds value.