# Gradient Descent and Optimization
Status: public
Confidence: medium (0.72) (verified)
Last verified: 2026-05-22
Generation: ai_structured

## TL;DR

Gradient descent is the fundamental optimization algorithm for training machine learning models. It iteratively adjusts parameters in the direction of steepest descent of the loss function. Variants: Batch GD (full dataset), Stochastic GD (single example), Mini-batch GD (small batches — standard).

## Core Explanation

Learning rate: controls step size — too small (slow convergence), too large (divergence). Momentum: accumulates past gradients for smoother steps. Adam optimizer: adaptive learning rates per parameter (combines momentum + RMSprop) — de facto standard (Kingma & Ba, 2014). Learning rate schedules: step decay, cosine annealing, warmup.

## Further Reading

- [Deep Learning (Goodfellow, Bengio, Courville)](https://www.deeplearningbook.org/)

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