Neural Network

What is a Neural Network?

A Neural Network is a mathematical function with trainable weights that transforms input data into output data through multiple successive layers of computation. The name is inspired by biological neurons, but the underlying function is pure mathematics.

How it works

A neural network consists of layers of artificial “neurons.” Each neuron computes a weighted sum of its inputs and applies an activation function that determines how strongly the result is passed to the next layer. Modern language models extend this basic structure with Self-Attention mechanisms that relate tokens to one another.

Why this structure can learn complex patterns

The structure of many simple computational steps allows the network to learn complex patterns that cannot be programmed using fixed rules. Training adjusts the weights between the neurons using example data. More layers and parameters can, in principle, distinguish finer patterns, although architecture, data quality, and training also play major roles.

A neural network does not “think” like a biological brain. The term “neuron” is an analogy from the early days of AI research. The actual computation consists of linear algebra followed by activation functions, not biological processes.

Why Neural Networks have practical implications

• Training effort: More layers and parameters require more computing power during training.
• Architectural diversity: Different network architectures are used for different tasks. Language models are based on the Transformer architecture with Self-Attention.
• No guarantee: A neural network produces statistically learned results rather than logically proven ones.

Typical example

A language model is a neural network with billions of parameters that processes text as sequences of numbers and computes a probability distribution over possible continuations for every token.

Further reading

Fundamentals

→ How a Language Model Works

Related terms

→ Parameter
→ Inference