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sgd

AI, ML & GenAI interview questions tagged sgd, across every topic.

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Concepts behind "sgd"

The curriculum that explains the ideas these questions test.

Foundational
📊 Evaluation & ML Foundations
Gradient Descent and OptimizersGradient descent is how models learn: compute the gradient of the loss with respect to the parameters and step opposite it to cut error. Mini-batch SGD (a small batch per step) is the workhorse, trading off stable gradients against speed and GPU parallelism. Momentum smooths the path, and Adam (momentum plus per-parameter adaptive rates) is the default. The learning rate is the most important knob, scheduled with warmup and decay. AI, ML, and GenAI engineer interviews probe it because it underlies all training and the failure modes (divergence, getting stuck) are diagnosable.
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