Optimization
term_id: optimization
Category: basic_concepts
Definition
In machine learning, optimization refers to the algorithms used to adjust model parameters to minimize a loss function, thereby improving model performance. Common methods include Gradient Descent and its variants like Adam or SGD. The goal is to navigate the parameter space efficiently to find global or local minima, ensuring the model generalizes well to unseen data by reducing the discrepancy between predicted and actual outputs.
Summary
The mathematical process of minimizing or maximizing an objective function to find the best solution parameters.
Key Concepts
- Loss function minimization
- Gradient descent
- Learning rate
- Convergence
Use Cases
- Training neural networks
- Hyperparameter tuning
- Resource allocation problems