Parameters
Adjustable values that determine AI model behavior
Overview
Parameters are the adjustable values within an AI model that determine its behavior and capabilities.
What are Parameters?
Parameters are the adjustable values that define model behavior. They are learned during training and include weights, biases, and other learnable values that enable the model to perform its task.
How Do They Work?
Parameters are adjusted during the training process through optimization algorithms like gradient descent. They store the model's learned patterns and relationships, allowing it to make predictions or generate outputs.
Types and Applications
- Weights in neural networks
- Biases in model layers
- Embedding matrices
- Attention weights
- Convolutional filters
- Recurrent cell states