Weight

Weights are learned during model training as the model adjusts its parameters to reduce prediction errors. In neural networks, each connection between neurons has a weight that controls the strength of the signal passed between them. The collection of weights represents much of the learned behavior of a trained model.

Weights allow machine learning models to learn relationships and patterns from data. Adjusting them during training enables the model to improve its predictions. Unauthorized access to or manipulation of model weights can also create security and intellectual property risks.