Neural Network
Core ConceptsA computing system inspired by the human brain, consisting of interconnected nodes (neurons) organized in layers that process information.
A neural network is the fundamental building block of modern AI. Inspired by biological neurons, it consists of layers of mathematical functions (nodes) that transform input data into useful output. Information flows through the network, getting processed at each layer.
The simplest neural networks have three layers: input, hidden, and output. Deep neural networks have many hidden layers (hence 'deep learning'). Convolutional neural networks (CNNs) specialize in images. Recurrent neural networks (RNNs) handle sequences. Transformers (the architecture behind LLMs) are a specific type of neural network.
You don't need to understand neural network math to use AI tools, but knowing the concept helps you understand why AI behaves the way it does — including its strengths (pattern recognition, generation) and weaknesses (hallucination, brittleness to unusual inputs).
Real-World Example
Every AI tool you use runs on neural networks — the mathematical architecture that enables machines to learn patterns from data.
Related Terms
More in Core Concepts
FAQ
What is Neural Network?
A computing system inspired by the human brain, consisting of interconnected nodes (neurons) organized in layers that process information.
How is Neural Network used in practice?
Every AI tool you use runs on neural networks — the mathematical architecture that enables machines to learn patterns from data.
What concepts are related to Neural Network?
Key related concepts include Deep Learning, Machine Learning, Transformer, Parameters, Training. Understanding these together gives a more complete picture of how Neural Network fits into the AI landscape.