Clear thinking about intelligent tools.
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.
Multiclass and multilabel tasks have different assumptions. Decide whether an example may receive several labels before choosing data structures or evaluation.
Set up early stopping with a clear monitored metric, patience rule and checkpoint decision while keeping the final test set untouched.
Learning curves compare training and validation scores as the training set grows. Use them to plan the next experiment without treating one plot as a verdict.