Definition
Loss functions, also known as cost functions, measure how well a machine learning model’s predictions match the ground truth during training. The goal of the optimization algorithm is to minimize this loss value. Different tasks require different loss functions; for example, Mean Squared Error (MSE) is common for regression, while Cross-Entropy is standard for classification. Monitoring loss helps diagnose issues like underfitting or overfitting.
Summary
A numerical value that quantifies the error between a model’s predictions and the actual target values.
Key Concepts
- Cost Function
- Optimization
- Gradient Descent
- Error Metric
Use Cases
- Training image classifiers
- Optimizing regression models
- Evaluating model convergence
Code Example
| |