A neural network is a machine-learning model made of layers of interconnected nodes (“neurons”) that transform input data into output by multiplying it against learned weights and passing it through nonlinear functions. It learns by adjusting those weights to reduce error on training examples. Neural networks are the foundation of deep learning and, by extension, of the transformer models behind today’s large language models.
Key takeaways
- A neural network is layers of simple units connected by weighted links that together learn complex patterns.
- Its knowledge lives in its weights — numbers adjusted during training, not rules written by a programmer.
- It learns through backpropagation and gradient descent, nudging weights to reduce prediction error.
- “Deep learning” just means neural networks with many layers — the family that includes transformers.
Neural networks, defined
A neural network is a mathematical model loosely inspired by the brain. It is built from units called neurons, arranged in layers. Each neuron takes numbers in, multiplies them by weights, adds them up, and passes the result through a nonlinear activation function that decides how strongly the neuron “fires.” Stack enough of these units in enough layers and the network can represent astonishingly complex relationships.
The essential shift neural networks introduced is that behavior is learned, not programmed. Rather than writing rules to recognize a cat or translate a sentence, you show the network many examples and let it adjust its weights until it produces the right outputs. The rules end up encoded implicitly in millions or billions of weight values.
Layers, weights, and activations
Information flows through a network layer by layer. An input layer receives the data, one or more hidden layers transform it into progressively more useful representations, and an output layer produces the final result — a classification, a number, or, for a language model, a probability over the next token. Each connection between neurons carries a weight, and it is the full set of weights that defines what the network computes.
Activation functions are what let networks learn nonlinear patterns. Without them, stacking layers would be no more powerful than a single linear step. With them, each layer can bend and combine features in ways that capture real-world complexity — curves, interactions, and hierarchies of meaning that simple linear models cannot express.
How a network learns
Training is an optimization loop. The network makes a prediction, a loss function measures how wrong it was, and an algorithm called backpropagation computes how each weight contributed to that error. Gradient descent then nudges every weight a little in the direction that reduces the error. Repeat this across millions of examples and the weights gradually settle into values that make good predictions.
This is exactly how an LLM acquires its abilities: it is a very large neural network trained to predict the next token, its weights tuned over trillions of examples. Nothing about grammar or facts is coded in by hand — it all emerges from this repeated cycle of predict, measure error, and adjust.
From neural networks to transformers
“Deep learning” simply refers to neural networks with many hidden layers. Depth lets a network build hierarchical representations — early layers capture simple features, later layers combine them into abstract concepts. This depth, powered by large datasets and GPUs, is what made modern AI take off.
The transformer is a particular deep neural network architecture specialized for sequences, using attention instead of the recurrence or convolution of earlier designs. So when people talk about LLMs, they are talking about a specific, very large kind of neural network — the concepts on this page are the foundation everything else is built on.
From concept to a governed, on-premise reality
Every model VDF AI runs — language models, embedding models, rerankers — is a neural network. VDF AI’s role is to run these networks inside your own infrastructure, under governance, rather than as a black box behind someone else’s API.
Understanding that a model’s behavior is learned from data, not hand-coded, is also why VDF AI emphasizes evaluation, guardrails, and audit logging: probabilistic, learned systems need a governance layer around them to be trustworthy in production.
Frequently asked questions
What is a neural network in simple terms?
It is a model made of layers of simple connected units that learn to transform inputs into outputs by adjusting numeric weights. Instead of being programmed with explicit rules, it learns patterns from examples during training.
How does a neural network learn?
Through training: it makes predictions, a loss function measures the error, backpropagation determines how each weight contributed, and gradient descent adjusts the weights to reduce the error. Repeating this over many examples gradually improves the network.
What are weights in a neural network?
Weights are the numbers on the connections between neurons. They determine how input signals are combined and are what the network adjusts during training. Collectively, the weights encode everything the network has learned.
What is the difference between a neural network and deep learning?
Deep learning is simply the use of neural networks with many hidden layers. The extra depth lets the network build hierarchical, abstract representations, which is what powers modern applications like image recognition and language models.
How do neural networks relate to LLMs?
An LLM is a very large neural network — specifically a transformer — trained to predict the next token. All the core ideas of neural networks (layers, weights, activations, training) apply directly to how LLMs are built and how they learn.
Put these concepts to work on infrastructure you control.
VDF AI runs governed agents, private retrieval, and model routing inside your own cloud, data center, or air-gapped network. Book a walkthrough mapped to your stack.