Clear thinking about intelligent tools.
Understand AI through careful examples, practical evaluation and the questions that matter beyond a demonstration.
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Mixture-of-experts models report total and active parameters for different reasons. Understand routing without mistaking experts for separate subject specialists.
A KV cache reuses attention information during language-model generation. Learn what it saves, what memory it consumes and what it does not remember.
Top-p sampling selects a changing set of likely next tokens. A small numerical example explains how it differs from choosing a fixed number of candidates.
Understand what PCA’s components and explained variance describe, why feature scaling matters, and why a clean two-dimensional plot does not establish causes.
DBSCAN builds clusters from local density and can leave points unassigned. Understand eps, min_samples and the limits before interpreting the labels.
K-means uses distances between feature values. A worked example shows how changing measurement scales can change the clusters a model finds.