https://github.com/khuyentran1401/numerical-optimization-machine-learning
Codes for popular numerical optimization methods and machine learning algorithms
https://github.com/khuyentran1401/numerical-optimization-machine-learning
determinant-calculation gauss-elimination linear-algebra machine-learning machine-learning-algorithms matrix normalization numerical-optimization python3 vectors
Last synced: about 1 month ago
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Codes for popular numerical optimization methods and machine learning algorithms
- Host: GitHub
- URL: https://github.com/khuyentran1401/numerical-optimization-machine-learning
- Owner: khuyentran1401
- Created: 2020-01-24T00:27:49.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2020-07-31T16:05:48.000Z (almost 5 years ago)
- Last Synced: 2025-03-27T03:51:19.729Z (about 2 months ago)
- Topics: determinant-calculation, gauss-elimination, linear-algebra, machine-learning, machine-learning-algorithms, matrix, normalization, numerical-optimization, python3, vectors
- Language: Jupyter Notebook
- Homepage:
- Size: 45.9 KB
- Stars: 13
- Watchers: 2
- Forks: 5
- Open Issues: 0
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Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# What is Linear Algebra?
Linear Algebra is about working on linear systems of equations. Rather than working with scalars, we start working with matrices and vectors.
Linear Algebra is the key to understanding the calculus and statistics you need in machine learning. If you can understand machine learning methods at the level of vectors and matrices, you will improve your intuition for how and when they work. The better linear algebra will lift your game across the board.
# What is in this Repository?
- Implementation of matrix and its methods such as finding the determinant, normalization
- Numerical optimization methods:
+ Direct methods:
1. Gauss Method
2. Crammer's Rule
+ Iterative methods:
1. Jacobi method
2. Gauss Seidel method