Stochastic Gradient Descent
Stochastic Gradient Descent (SGD) is an optimization algorithm that updates a model's weights using the gradient calculated from a single randomly chosen training sample (or a small batch) rather than the entire dataset.
Frequently Asked Questions
How does SGD differ from Batch Gradient Descent?▼
Batch Gradient Descent computes gradients on the entire dataset before making one update, which is slow and memory-intensive. SGD updates weights much faster by using single samples or mini-batches.
Why does SGD have a noisy or fluctuating optimization path?▼
Because it estimates the true gradient using only a subset of data, which introduces variance. This noise can actually help the optimizer escape poor local minima.
Quick Facts
- CategoryMathematical Foundations
- Key ApplicationTraining deep learning models, online learning systems, and scaling optimization on massive datasets.
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