This repository contains code examples and explanations for various mathematical concepts used in machine learning. It covers topics such as linear algebra, calculus, probability and statistics, and numerical methods.
The goal of this repository is to provide a comprehensive resource for understanding the mathematical foundations of machine learning. It aims to explain key concepts and provide code examples that demonstrate their application in machine learning algorithms.
To fully understand the material presented in this repository, it is recommended to have a basic understanding of programming (preferably Python) and familiarity with machine learning concepts.
The repository covers the following mathematical topics frequently used in machine learning:
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Linear Algebra
- Vectors and Matrices
- Matrix Operations (Addition, Subtraction, Multiplication)
- Matrix Transpose and Inverse
- Eigenvalues and Eigenvectors
- Singular Value Decomposition (SVD)
- Principal Component Analysis (PCA)
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Calculus
- Functions
- Limits
- Contour PLot
- Chain Rule
- Derivative
- Newton-Raphson
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Probability & Statistics
- Probability
- Descriptive Statistics
- Inferential Statistics
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Optimization
- First-Order Methods
- Second-Order Methods
Contributions to this repository are welcome! If you find any issues, have suggestions for improvements, or would like to add new content, please feel free to submit a pull request. Please ensure that your contributions align with the focus of the repository and follow the existing structure and style.