Gradient Descent

Gradient Descent is the optimization algorithm that drives most modern AI training — iteratively adjusting model parameters to reduce error. The basic idea is intuitive: compute the slope (gradient) of the error with respect to each parameter, then take a small step in the direction that reduces error. Repeat millions of times and the model learns. Variants are everywhere in practice: Stochastic Gradient Descent (SGD) processes one example at a time, mini-batch SGD processes small groups, and adaptive methods like Adam, AdamW, and RMSprop adjust the step size per parameter automatically. These workhorses train everything from simple linear regression to GPT-scale models. Choices like learning rate, momentum, and learning-rate schedules can make or break a training run. AI governance programs require documenting optimization choices for AI compliance and reproducibility, supporting responsible AI and AI risk management — particularly when models are retrained or fine-tuned later.

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