This project implements two popular logistic regression algorithms—Softmax Regression and One-vs-All Classification—applied to the MNIST dataset. The MNIST dataset consists of handwritten digit images, commonly used for training various image processing systems.
- Softmax Regression: A generalization of logistic regression for multi-class classification problems.
- One-vs-All Classification: An approach where multiple binary classifiers are trained to distinguish each class against all others.
- Performance Analysis: Detailed evaluation of both algorithms on the MNIST dataset.
For detailed analyses, results, and comparisons of the algorithms, refer to the Analysis.pdf file in the repository.