Training & Fine-tuning
Loss Function
The measure of how wrong a model's outputs are — training aims to minimise this.
Definition
A loss function quantifies the difference between what the model predicted and what the correct answer was. A high loss means the model is doing poorly; a low loss means it's doing well. Training is the process of progressively reducing the loss across all the examples in the training dataset. Different types of tasks use different loss functions suited to the nature of the task and what constitutes an 'error.'
Related Terms
Gradient Descent
The algorithm models use to gradually improve during training by reducing errors.
Backpropagation
The process of passing error signals backwards through a network to update weights.
Supervised Learning
Training where the model learns from labelled examples — input paired with the correct answer.
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Disclaimer
This definition is provided for educational and informational purposes only. It represents a general explanation of a technical concept and does not constitute professional, technical, or investment advice. Artificial intelligence is a rapidly evolving field; terminology, techniques, and capabilities change frequently. Coaley Peak Ltd makes no warranty as to the accuracy, completeness, or currency of the information provided. Nothing on this page should be relied upon as the sole basis for commercial, technical, legal, or investment decisions without independent professional advice.
Document reference: ISO_webpage_knowledge-base_glossary_v1
Last modified: 29 March 2026
Knowledge Base·Training & Fine-tuning·Loss Function