Linear-Algebra
Overview
This repository is dedicated to exploring various concepts of Linear Algebra using Numpy in Python, illustrated through Jupyter Notebooks.
Detailed File Descriptions
01. Introduction.ipynb
: Introduction to basic linear algebra concepts.02. Index, Slice, Reshape Numpy array.ipynb
: Demonstrates array manipulation techniques in Numpy.03. Broadcasting in Numpy.ipynb
: Explains broadcasting in Numpy for array operations.04. Vectors in Numpy.ipynb
: Covers vector operations in Numpy.05. Vector Norm.ipynb
: Discusses the concept and calculation of vector norms.06. Matrices and Matrix Arithmetic.ipynb
: Basics of matrix operations in Numpy.07. Types of Matrices.ipynb
: Differentiates various types of matrices.08. Matrix Operation.ipynb
: Advanced matrix operations and techniques.09. Sparse Matrices.ipynb
: Handling sparse matrices in Numpy.10. Tensors and Tensor Arithmetic.ipynb
: Introduction to tensors and their operations.11. Matrix Decomposition.ipynb
: Methods of matrix decomposition.12. Eigen Decomposition.ipynb
: Exploring eigenvalues and eigenvectors.13. Singular Value Decomposition.ipynb
: Implementation of singular value decomposition.14. PseudoInverse.ipynb
: Understanding and calculating the pseudoinverse of matrices.15. Dimensionality Reduction using SVD.ipynb
: Using singular value decomposition for reducing dimensions.16. Multivariate Statistics.ipynb
: Principles of multivariate statistics.17. Covariance.ipynb
: Techniques for calculating covariance.
License
This project is licensed under the MIT License.
Acknowledgements
Thanks to all contributors and the mathematics community.