Training & Fine-tuning
Backpropagation
The process of passing error signals backwards through a network to update weights.
Definition
Backpropagation is the algorithm that allows neural networks to learn. After the model makes a prediction and the loss is calculated, backpropagation determines how much each parameter in the network contributed to the error. This information is passed backwards through the layers, and gradient descent uses it to update the parameters in a direction that reduces the error. Without backpropagation, training deep neural networks would be computationally intractable.
Related Terms
Gradient Descent
The algorithm models use to gradually improve during training by reducing errors.
Loss Function
The measure of how wrong a model's outputs are, training aims to minimise this.
Neural Network
The underlying computational structure that most modern AI is built on, layers of interconnected mathematical nodes.
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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·Backpropagation