AI Dictionary
Gradient Descent
Definition
An optimization algorithm used to minimize some function by iteratively moving in the direction of steepest descent.
Deep Dive
Gradient Descent is a fundamental optimization algorithm used in machine learning to minimize a function, typically a cost or loss function, by iteratively moving in the direction of the steepest descent. Imagine being at the top of a hill in the dark and trying to find your way to the valley floor by taking small steps in the direction that slopes downwards the most. In machine learning, this "hill" represents the loss function, and the "valley floor" is the set of model parameters (weights and biases) that minimize this loss.
Examples & Use Cases
- 1Training a linear regression model to find the optimal coefficients that minimize the mean squared error between predicted and actual values
- 2Optimizing a neural network during backpropagation to adjust the weights and biases of its layers, reducing the loss function and enhancing prediction accuracy
- 3Fitting a polynomial curve to a dataset by iteratively adjusting the curve's coefficients to minimize the error between the polynomial and the observed data points
Related Terms
Optimization AlgorithmLoss FunctionBackpropagation