Neural network

A neural network is a computational model built from layers of interconnected nodes, also called neurons. It is loosely inspired by the human brain and forms the engine behind much of modern machine learning. By making connections stronger or weaker, the network learns which input leads to which outcome.

How does such a network work?

Data enters in the first layer, is passed to hidden layers that perform calculations, and leaves the network as a prediction in the final layer. During training, the model compares each prediction with the correct answer and adjusts the weights of the connections a little. That process repeats countless times, until the network reliably recognizes the right patterns in new data it has never seen.

The link with deep learning

A network with many hidden layers is called a deep network, and that is where the term deep learning comes from. The more layers, the more complex the patterns it can capture. The language models behind ChatGPT, Gemini and Claude are essentially enormous neural networks, and a large language model is also built on this building block.

Why it matters to marketers

You do not need to train a network to benefit from one, but understanding how they work helps you stay realistic. A neural network is only as good as the data you feed it, and it rarely gives an explanation for its answer. For a B2B company, what counts is therefore not how advanced the technology sounds, but whether it brings you closer to real customers and revenue. Steer on that goal, not on the figure that merely looks good.