What does Neural Network mean?

A neural network is a computational model made of many simple units, arranged in layers and connected through weighted links. Each unit adds up its input values, applies an activation function, and passes the result on to the next layer. The term comes from biology, but the calculation itself has little in common with how nerve cells actually work.

A network consists of an input layer, several hidden layers, and an output layer. Activation functions such as ReLU let the network capture relationships that are not straight lines, rather than being limited to linear patterns. During training, gradient descent shifts the weights step by step so the error gets smaller. Convolutional networks are well suited to images, while sequences such as text or time series are today mostly handled by transformers.

A neural network pays off wherever many factors interact and their relationship cannot be written as a formula. Examples include predicting sensor readings, recognizing objects in images, and estimating the remaining service life of technical equipment. With few features and a clear structure, decision trees deliver comparable results at lower effort and with a transparent rule.

The advantage over a manually built formula is that the weighting comes from the data instead of being estimated. As new data arrives, the model is retrained without changing its structure. The gap between prediction and actual value can be measured for every single case.

A neural network produces a number, not a justification. Wherever a decision affects people, additional methods from explainable AI are needed, or a simpler model with a transparent rule. Which path fits depends on the review requirements of the specific process.