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adam

Applied AI interview questions tagged adam, across every topic.

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Concepts behind "adam"

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 reduce error. Mini-batch SGD (a small batch per step) is the workhorse, balancing stable gradients with 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. Applied-AI interviews probe it because it underlies all training and the failure modes (divergence, getting stuck) are diagnosable.
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