Gradient Descent
An optimization algorithm that iteratively adjusts model parameters to minimize the loss function.
In-depth explanation
Gradient descent calculates the gradient (slope) of the loss function with respect to each parameter and takes steps in the direction that reduces the loss. Variants include batch gradient descent (uses all data), stochastic gradient descent (uses one sample), and mini-batch gradient descent (uses small batches). Learning rate controls step size.
Examples
Related terms
More in Machine Learning
Classification
Predicting which category or class an input belongs to from a set of predefined categories.
Cross-Validation
A technique to evaluate model performance by training and testing on different subsets of data.
Ensemble Learning
Combining multiple models to produce better predictions than any single model.
Feature
An individual measurable property or characteristic of data used as input to a machine learning model.
Feature Engineering
The process of using domain knowledge to create new features that improve model performance.
Hyperparameter
Configuration settings set before training that control the learning process, not learned from data.
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