Figure 1: A three-dimensional Euclidean space used to represent solutions of linear equations [1] [2]. Image is a vector derivative from “High-dimensional Simplexes for Supermetric Search” by Richard Connor, Lucia Vadicamo, and Fausto Rabitti [3].

Deep Learning, Editorial, Machine Learning, Mathematics, Programming, Tutorial

Basic Linear Algebra for Deep Learning and Machine Learning Python Tutorial

An introductory tutorial to linear algebra for machine learning (ML) and deep learning with sample code implementations in Python

Last updated, January 6, 2021

Author(s): Saniya Parveez, Roberto Iriondo

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This tutorial’s code is available on Github and its full implementation as well on Google Colab.

Table of Contents

1. Introduction
2. Linear Algebra in Machine Learning and Deep Learning
3. Matrix
4. Vector
5. Matrix Multiplication
6. Transpose Matrix
7. Inverse Matrix
8. Orthogonal Matrix
9. Diagonal Matrix
10. Transpose Matrix and Inverse Matrix in Normal Equation
11. Linear Equation
12. Vector Norms
13. L1 norm or Manhattan Norm
14. L2 norm or Euclidean Norm
15. Regularization in Machine Learning
16. Lasso
17. Ridge
18. Feature Extraction and Feature Selection
19. Covariance Matrix
20. Eigenvalues and Eigenvectors
21. Orthogonality
22. Orthonormal Set
23. Span
24. Basis
25. Principal Component Analysis (PCA)
26. Matrix Decomposition or Matrix Factorization
27. Conclusion
28. Resources
29. References

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