Linear Algebra Course Project

Welcome to the Linear Algebra Course Project! This repository contains two Jupyter notebooks, each covering a different topic related to linear algebra: one focusing on Computer Vision and the other on Natural Language Processing (NLP).

Course Overview

The Linear Algebra course covers fundamental concepts in linear algebra, including vectors, matrices, systems of linear equations, eigenvalues, eigenvectors, and various applications in computer science, engineering, and other fields.

Project Notebooks

1. Computer Vision

• Notebook Name: Project_Vision (1).ipynb
• Description: This notebook explores the application of linear algebra concepts in computer vision tasks. It covers topics such as image representation, transformations, convolutional operations, and feature extraction using techniques like Singular Value Decomposition (SVD) and Principal Component Analysis (PCA).

2. Natural Language Processing (NLP)

• Notebook Name: Project_NLP.ipynb
• Description: This notebook delves into the application of linear algebra in natural language processing tasks. It discusses techniques such as word embeddings, document-term matrices, semantic similarity, and sentiment analysis using linear algebra operations like vector space models and matrix factorization.

Usage

To view and interact with the notebooks, you can open them in Jupyter Notebook or JupyterLab. Simply navigate to the project directory and launch Jupyter by running jupyter notebook or jupyter lab command in your terminal.

Dependencies

Both notebooks require the following dependencies:

• Python (version X.X.X)
• Jupyter Notebook or JupyterLab
• NumPy
• Matplotlib
• Pandas
• [Any additional libraries used]