Knowledge Graph — Coursera Notes › Academic disciplines

Linear Algebra

concept · part of Academic disciplines

Linear algebra is a branch of mathematics that deals with vectors, vector spaces, linear transformations, and systems of linear equations. It provides the foundational framework for representing and manipulating data in neural networks and other AI/ML models, where inputs, weights, and outputs are treated as vectors and matrices. Operations like matrix multiplication, dot products, and eigenvalue decomposition are essential for forward propagation, backpropagation, and optimization in deep learning. Linear algebra is used extensively in training neural networks, dimensionality reduction (e.g., PCA), and natural language processing (e.g., word embeddings). As the parent concept for neural networks, it underpins the mathematical operations that enable learning from data, while related concepts like calculus and probability theory complement it for optimization and uncertainty modeling.

Connections

• Prerequisite of Neural Network

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