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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteAn artificial neural network (ANN) is a computational model that turns inputs into outputs through interconnected mathematical processing units. During training, it adjusts internal parameters so its outputs better match examples or targets. The units are loosely inspired by biological neurons, but they are mathematical operations—not miniature brains.
What an artificial neural network does
A neural network learns patterns in data to perform a task such as classifying an image or estimating a value. It receives input values, transforms them through connected computations, and produces an output. The connections and computations are controlled by parameters learned during training.
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In a common feed-forward network, information moves from an input layer through one or more hidden layers to an output layer. The input layer represents features supplied to the model; hidden layers compute intermediate representations; and the output layer produces the result. This is one common arrangement, not a rule that every network follows. Layers may differ in how they connect and process information.
How a network processes information
A simplified unit computes a weighted sum of its inputs, adds a bias, and applies an activation function:
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a = σ(Σ wᵢxᵢ + b)
- Inputs (xᵢ): values passed into the unit.
- Weights (wᵢ): learned values that determine how strongly each input affects the computation.
- Bias (b): a learned offset added to the weighted sum.
- Activation function (σ): transforms the sum. Nonlinear activation functions allow a network to represent more than a simple linear relationship.
The outputs of units in one part of the network can become inputs to later computations. In this way, layers can build intermediate representations before the network produces its final output. The formula is a teaching simplification: real architectures may use other operations and connection patterns, and not every layer is fully connected.
How neural networks learn
In a typical supervised-learning setup, training examples include inputs and target outputs. The network adjusts its parameters to reduce the difference between its predictions and those targets.
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- Make a prediction: A forward pass carries an input through the network to produce an output.
- Measure the error: A loss function compares that prediction with the target and assigns a value to the mismatch.
- Calculate parameter gradients: Backpropagation applies the chain rule to determine how changes in weights and biases affect the loss.
- Update the parameters: An optimizer, often gradient descent or a variant, uses the gradient information to adjust the parameters.
- Repeat and evaluate: Training repeats across examples and iterations. Performance should also be assessed on data that was not used to fit the model.
These are distinct parts of the process: the loss defines what the training procedure is trying to minimize, backpropagation calculates gradients, and the optimizer uses those gradients to update parameters.
How neural networks relate to AI and deep learning
A useful introductory hierarchy is artificial intelligence (AI) as the broad field, machine learning as a collection of methods within AI, and neural networks as one family of machine-learning models. Deep learning commonly refers to learning with neural networks that have multiple hidden layers. The terminology is not perfectly uniform in every context, but the distinctions help: not all AI uses neural networks, not all machine learning is deep learning, and not every neural network is deep.
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What the term does—and does not—mean
“Neural” refers to a loose inspiration from nervous systems, not a claim that artificial units reproduce biological neurons. An ANN is a computational model that learns relationships from data. Its learned behavior depends on its architecture, training examples, objective, and parameter updates; it does not follow from the analogy to a brain.
Network architectures also vary. Feed-forward describes a connection pattern in which information moves through the network without cycling back through recurrent connections. Other designs use different patterns or operations to handle different structures in data. No single architecture is best for every task, and this basic definition alone is not enough to choose one for a particular application.
Why performance on training data is not enough
A network can fit its training examples yet perform poorly on new ones. This is called overfitting: the model has learned details of the training data that do not transfer reliably. Evaluation on data not used for fitting helps reveal whether a model generalizes beyond the examples it saw during training. Adding layers alone does not guarantee better performance.
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IBM’s neural-network explainer introduces the model, training, and overfitting. For a textbook-style introduction, see OpenStax’s chapter on neural networks. The NCBI Bookshelf chapter on neural networks and deep learning discusses the relationship between multilayer networks and deep learning. IEEE Technology Navigator offers a concise definition of artificial neural networks; Emory University provides an introductory explanation of feed-forward neural networks.
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