The Fundamental Mathematics of Machine Learning

Author(s): Joseph Robinson, Ph.D.

A Deep Dive into Vector Norms, Linear Algebra, Calculus

Originally published on Towards AI.

A visual depiction of linear algebra transformations is covered in this blog (source).

· Overview ∘ Brief Overview of the Importance of Math in ML· Importance of Math in Machine Learning ∘ Linear Algebra and Calculus in ML· Vector Norms· Linear Algebra in ML ∘ Basic Concepts: Vectors, Matrices, and Operations ∘ Practical Applications in ML· Calculus in ML ∘ Fundamental Concepts: Derivatives and Integrals ∘ Partial Derivatives and Gradients ∘ Chain Rule and Backpropagation ∘ Practical Applications in ML· Linear Algebra and Calculus in Model Training ∘ Linear Algebra in Model Training ∘ Calculus in Model Training ∘ Examples of Model Optimization Using These Math Concepts· Case Studies and Practical Examples ∘ Step-by-Step Walkthroughs of Specific Applications· Conclusion· References· Appendix ∘ Additional Mathematical Proofs and Detailed Examples· Call to Action

This blog explores the core mathematical concepts needed to understand and build machine learning (ML) models. We’ll dive into linear algebra and calculus, showing how they are used in model training and optimization. By the end, you’ll have a more precise grasp of these foundations and their practical applications.

Mathematics is the backbone of machine learning. Understanding the underlying mathematical principles behind algorithms allows you to grasp how models work, why they… Read the full blog for free on Medium.

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Published via Towards AI