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
Supervised Learning
Machine learning approach where models learn from labeled training data to predict outcomes.
Unsupervised Learning
Machine learning approach where models find patterns in data without labeled examples.
Semi-Supervised Learning
Machine learning approach using a small amount of labeled data with a large amount of unlabeled data.
Classification
Predicting which category or class an input belongs to from a set of predefined categories.
Regression
Predicting a continuous numerical value based on input features.
Feature
An individual measurable property or characteristic of data used as input to a machine learning model.
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