learning rate
AI, ML & GenAI interview questions tagged learning rate, across every topic.
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Concepts behind "learning rate"
The curriculum that explains the ideas these questions test.
Foundational
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.📊 Evaluation & ML Foundations
Core
Fine-Tuning Hyperparameters and OverfittingChoosing to fine-tune is the easy part. The run succeeds or fails on a short list of dials: learning rate, epochs, effective batch size, LoRA rank and alpha, which modules you target, and max sequence length. Overfitting is the default outcome when those dials are set by copying a blog post, and its signature is a train loss that keeps falling while eval loss turns up. AI, ML, and GenAI interviews probe this because it separates people who have actually run a fine-tune from people who have only read about one.🧠 Foundations of LLMs & GenAISign in