Mini toolsDefinition
The learned parameters of a neural network that determine its behavior. These weights represent the knowledge captured by the model during training.
Detailed Explanation
Model weights are the numerical parameters learned during training that define how input signals are transformed through the network. They include both the weights connecting neurons and bias terms. The values of these parameters represent the patterns and relationships the model has learned from training data. The number and organization of weights define the model's capacity and architecture.
Use Cases
Model deployment Transfer learning Fine-tuning Model sharing
