Principal Component Analysis (PCA) is a widely used method for dimensionality reduction in data science and machine learning. It transforms interdependent variables into fewer independent dimensions, making computation more efficient and revealing the dominant structure in complex datasets. PCA provides both mathematical clarity and practical value, helping to work effectively with high-dimensional data.

An end-to-end picture of neural networks as compositional functions—neurons, layers, and representations—showing how structure, notation, and careful computation turn high-dimensional inputs into stable, interpretable outputs.