Term tag: feature extraction

Convolutional Neural Network (CNN)

The architecture that assumes image features are local and position-independent — how the stack builds a hierarchy, and where CNNs still beat transformers.

Data Processing

What happens to raw data before a model can use it: ingestion, validation, transformation, ETL vs ELT, batch vs streaming, and what silently breaks.

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.

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.

Pattern Recognition (PR)

Pattern recognition is the automatic discovery and classification of regularities in data — the older name for what became statistical machine learning.

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.

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.