Deep learning
Machine learning with neural networks containing multiple processing layers.
What is deep learning?
Deep learning is a branch of machine learning using multilayer neural networks. Successive computational steps can learn suitable representations of inputs. Data, computing and training needs depend on the model and task. Greater depth alone is neither evidence of quality nor a reason to prefer this method over every simpler approach.
A fictional company wants to recognise hard-to-read product labels in photos. A deep-learning model is one possible approach. The team checks suitability with images not used for training and representative of use, including different lighting or damaged labels. It also compares simpler suitable methods and operational and checking effort. Good results on selected training photos alone do not establish reliable recognition in the intended workflow.
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Related terms
- Machine learningModels learn patterns from data and apply them to new inputs.
- Neural networkA model of connected computational nodes with adjustable parameters.
- 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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