Parameter
In artificial intelligence, a parameter is a value that is learned during model training. In neural networks, the most common parameters are weights and biases, which govern how input data is transformed into predictions.
Background
Parameters are different from hyperparameters, which are set before training (e.g., learning rate, number of layers). Modern AI models often contain millions or billions of parameters — large language models (LLMs) like GPT or PaLM are prime examples. Parameters are optimized through algorithms such as gradient descent, which minimizes the loss function.
Examples
- Neural networks: weights adjusted to detect objects in images.
- Language models: billions of parameters enabling contextual understanding.
- Linear regression: coefficients of the regression line are parameters estimated from data.
Strengths and challenges
- ✅ Enable models to learn automatically from data.
- ✅ Larger parameter counts allow for higher representational capacity.
- ❌ Too many parameters increase risk of overfitting.
- ❌ Training requires massive computational resources.
In artificial intelligence, parameters are the internal values learned by a model during training. They define how inputs are processed and transformed into outputs. In neural networks, these parameters are mainly weights (which scale inputs) and biases (which shift them), forming the foundation of how the network learns patterns.
Parameters differ from hyperparameters, which are set before training begins—for example, the learning rate, batch size, or the number of layers in a neural network. While hyperparameters guide the learning process, parameters are the direct result of it.
Modern models, particularly large language models (LLMs) like GPT or BERT, can contain billions of parameters, which explains both their expressive power and their enormous computational demands. Training such models requires advanced optimization algorithms and vast amounts of data.
The advantage of having many parameters is that models can represent highly complex patterns. However, too many parameters can lead to overfitting, where the model memorizes training data instead of generalizing. This creates a constant trade-off between capacity, generalization, and resource requirements.
📚 Further Reading
- Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning.
- Bishop, C. M. (2006). Pattern Recognition and Machine Learning.
