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How is Linear Algebra Applied in Machine Learning
Starting from using matrix and vector for data representation
Truth be told, the role of linear algebra in machine learning has been perplexing me, as mostly we learn these concepts (e.g. vector, matrix) in a math background while discarding their applications in the machine learning context. In fact, linear algebra has several foundational use cases in machine learning, including data representation, dimensionality reduction and vector embedding. Starting from introducing the basic concepts in linear algebra, this article will build an elementary view of how these concepts can be applied for data representation, such as solving a linear equation system, linear regression, and neural networks. However, if you would like to know more about linear algebra for Principal Component Analysis (PCA), you may find this article more helpful.
For a video walkthrough of linear algebra for machine learning, I have included my YouTube video at the bottom of this article.
