Look up any term in AI.

Fine-tuning (FT)

What fine-tuning changes inside a model, what it costs in GPU memory, and why it reliably buys format and style but not new facts.

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.

Classification (CLF)

Accuracy is usually the wrong metric. What a confusion matrix, precision, recall and the decision threshold really tell you about a classifier.

Knowledge Distillation

How a small model learns what a big one knows: the teacher's full probability distribution carries structure the hard labels do not — Hinton's dark knowledge.

Sycophancy

Sycophancy is when an AI model tells you what you want to hear — agreeing, flattering, caving when challenged — because human-preference training rewards it.

Transfer Learning (TL)

Transfer learning reuses a model trained on one large task as the starting point for a related task, so you need far less labeled data and compute.