Look up any term in AI.

Pre-trained Models

A neural network someone else already trained on a huge general dataset, which you download and adapt to your task instead of training from scratch.

High Bandwidth Memory (HBM)

Stacked DRAM bonded beside the GPU die — the scarce component that gates AI accelerator supply, and why bandwidth rather than capacity is the constraint.

AI Healthcare

Where AI is actually deployed in medicine — imaging, triage, ambient notes — and why 'beats doctors' headlines rarely survive contact with a clinic.

Dimensionality Reduction (DR)

Why high-dimensional data has to be compressed, how PCA picks its axes, and why cluster sizes and gaps in a t-SNE or UMAP plot mean nothing.

Class Imbalance

Why a fraud model can score 99% accuracy and catch nothing — the imbalance trap, why precision/recall/PR-AUC replace accuracy, and how to fix it.

Regularization

Regularization deliberately constrains a model so it fits training data less perfectly, trading a little training accuracy for much better generalization.