Group 13_Topic 8_ Applied Math _ Applied Mathematical Optimization
Course: DSC 210: Numerical Linear Algebra for Data Science
Instructor: Dr. Tsui-wei Weng
Instructions:
This project implements various forms of the gradient descent algorithm in Python, using NumPy and Matplotlib for calculations and visualizations. It includes implementations of standard Gradient Descent (GD), Mini-Batch Gradient Descent, Stochastic Gradient Descent (SGD), and the Adam optimization algorithm.
Both Jupyter notebooks are self-contained, providing instructions and explanations alongside the code chunks. "Prestudy" serves as an illustrative example demonstrating the differences between optimizers using simple datasets. On the other hand, "MNIST_Optimizer" presents a more comprehensive experiment involving the MNIST dataset.