Look up any term in AI.

Feature Scaling

Feature scaling puts numeric features on comparable ranges so no variable dominates by its units alone — why distance- and gradient-based models need it.

Concurrency

Concurrency is structuring a program so independent tasks interleave. How it differs from parallelism, and why serving a model is a queueing problem.

Principal Component Analysis (PCA)

Principal Component Analysis (PCA) reduces dimensions by projecting data onto orthogonal directions of maximum variance, ranked by the variance each keeps.

Multimodal AI

How one model reads an image and text at once: the picture is cut into fixed patches, each patch becomes a token, and those tokens cost context.

Supervised Fine-Tuning (SFT)

Supervised fine-tuning trains a pretrained language model on curated prompt-response pairs with next-token loss, teaching it to follow instructions.

Temperature

Temperature is the number that controls how random a language model's output is — it divides the logits before softmax, from near-deterministic to diverse.