What Is a Neural Network?

A neural network is a computing system loosely inspired by the brain, made of connected layers of simple units called nodes. Each connection carries a weight the system adjusts as it learns, so the network can recognize patterns in data. Neural networks are the core structure behind deep learning and most modern AI.

How a neural network works

A neural network passes data through layers of nodes. Each node takes in numbers, combines them using its connection weights, and passes a result to the next layer. The first layer receives the raw input, the last layer produces the output, and the layers in between reshape the data step by step.

Learning happens by adjusting the weights. During training, the network compares its output to the correct answer, measures the error, and nudges its weights to reduce it, repeating this across many examples. Over time the weights settle into values that let the network map inputs to the right outputs, which is how it comes to recognize an image or predict a word.

Why neural networks matter for AI

Neural networks matter because they can learn complex patterns directly from data, without a person writing rules for the task. Stacking many layers, the approach called deep learning, lets them handle problems like vision and language that resisted older methods. Nearly all of today's most capable AI, including large language models, is built on neural networks. At Custom AI Studio, the models we deploy are neural networks under the hood, tuned to a client's data and goals.