Gradient Descent
Gradient Descent is an optimization algorithm used to minimize a model's loss function during training. It iteratively calculates the slope (gradient) of the error surface and updates model parameters (weights) in the direction of the steepest descent.
Frequently Asked Questions
What is the learning rate in gradient descent?▼
A hyperparameter determining the step size taken towards the minimum. Too large can cause divergence; too small makes training extremely slow.
What is Stochastic Gradient Descent (SGD)?▼
A variant of gradient descent that calculates gradients and updates weights using only a single random training sample (or batch) at a time, speeding up updates.
Quick Facts
- CategoryMathematical Foundations
- Key ApplicationNeural network training, parameter optimization, and regression models
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