13. Parameter Optimization

In machine learning, parameter optimization is a critical process that involves fine-tuning the parameters of a model to minimize a predefined loss function. This optimization is essential for enhancing the model’s ability to accurately make predictions. Two fundamental concepts in this process are the loss function and gradient descent.

See also

Click the video above for a quick introduction to this section.

• 13.1. Loss function
• 13.2. Gradient descent