In machine learning and deep learning, a parameter refers to a variable within a model that the training process adjusts to optimize the model’s performance. Parameters are the core elements that determine how a model processes input data and produces predictions.
Key Characteristics:
- Learnable Variables: Parameters are updated during training to minimize a loss function, improving the model’s accuracy.
- Weights and Biases: Common examples of parameters in neural networks include weights (connections between neurons) and biases (offsets added to the weighted sum).
- Model Complexity: The number of parameters in a model reflects its capacity to learn complex patterns from data.
- Fixed Post-Training: Once a model is trained, its parameters remain fixed during inference unless fine-tuning is applied.
Applications:
- Neural Networks: Parameters like weights and biases are adjusted during backpropagation to improve model predictions.
- Transfer Learning: Pre-trained model parameters are reused and fine-tuned for specific tasks or domains.
- Hyperparameter Optimization: While parameters are learnable, hyperparameters (e.g., learning rate) are set manually to guide training.
- Transformer Models: Parameters in large language models (e.g., GPT, BERT) include billions of weights and biases, enabling them to process complex language tasks.
Why It Matters:
Parameters are the foundation of machine learning models, directly influencing their performance, efficiency, and generalization. Understanding parameters is crucial for improving model accuracy, diagnosing issues, and optimizing resources.
