Linear Algebra Usage Introduction: Linear Algebra is the primary computation tool in both Artificial Intelligence (AI) and **Quantum Computing (QC). **In this blog will cover the required topics for storage and computing for both the fields.

Linear Algebra Concepts

Linear Algebra is basically a Mathematical Computational Tool and widely used in many areas like science , engineering and computational fields. Following diagram describes the concepts used in Linear Algebra for AI and Quantum Computing, first 4 topics (Vectors, Matrices, Tensors and Product Rules) covered in this article and the remaining topics are Decomposition ,**Advanced Concepts **for Quantum Computing, **Matrix Calculus **for Machine Learning and Deep Learning in other articles.

**Note: **Decomposition and Matrix Calculus links updated soon.

Primarily your data has to be stored in some where else to compute, Linear Algebra stores your data in Vectors, Matrices and Tensors and then operate as per your instructions. Linear Algebra is a Storage Space.

Title diagram shows what are mathematical objects as well as AI sub fields and Quantum Computing uses Linear Algebra.

Artificial Intelligence: Linear Algebra can be applied in Machine Learning and Artificial Intelligence, including exposure to clustering, data fitting, classification, validation, and feature engineering.

Linear algebra applied in unsupervised learning methods (like k-means) such as principal component analysis (PCA), non-negative matrix factorization and more sophisticated clustering methods. You can apply regression and classification methods , such as logistic regression and the support vector machine, as well as methods for computing model parameters that scale to extremely large scale problems.

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