Neural network
A model of connected computational nodes with adjustable parameters.
What is a neural network?
A neural network connects computational nodes, often in several layers. Weights determine input influence; activation functions transform combined values and can enable nonlinear relationships. Training adjusts suitable model parameters. The name reflects biological inspiration but does not describe a human brain. Later application to new inputs is not automatically further training.
In a fictional business, a network is to recognise a single handwritten digit in a form image. It produces a prediction from image data. Unclear handwriting is to receive appropriate review. Even a formally suitable digit does not establish that the document was assigned to the correct job. Recognition and business assignment therefore each need suitable checks.
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Related terms
- Machine learningModels learn patterns from data and apply them to new inputs.
- Deep learningMachine learning with neural networks containing multiple processing layers.
- Large language modelA large language model processes language using learned patterns. Fluent text proves neither factual accuracy nor access to current business data.
- Model cardA model card documents an AI model’s purpose, foundations, evaluation conditions and known limitations for a particular version.
- AI readinessAI readiness concerns the foundations of an AI project: objectives, usable data, skills, responsibilities and an operating workflow.
- Context engineeringContext engineering prepares information for individual model calls: relevant instructions, messages, sources and current states.
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