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Gradient Descent
An optimisation algorithm that iteratively updates model parameters to reduce prediction error.
Definition
An optimisation algorithm that iteratively updates model parameters to reduce prediction error.
How It Works
Gradient Descent helps teams build predictable AI and translation workflows by setting clear expectations for quality, consistency, and decision-making.
In production environments, this concept is applied with process controls such as human review, terminology alignment, and repeatable quality checks across multilingual content.
Key Concepts
- core principle of gradient descent
- workflow-level implementation
- terminology and quality consistency
- human validation before publication
Where It Is Used
- localisation workflows
- AI translation pipelines
- multilingual content production
- cross-referencing related concepts such as GDPR (General Data Protection Regulation)