Introduction
This repository contains a comprehensive collection of gradient descent algorithms implemented in Python from scratch. It serves as a valuable resource for those looking to understand and implement various optimization techniques used in machine learning and data science. The algorithms included range from basic to advanced, providing users with a solid foundation in gradient descent methods.
Key Features:
- Multiple Implementations: Explore various gradient descent algorithms including Stochastic Gradient Descent, Mini-batch Gradient Descent, and more.
- Python Code: All algorithms are implemented in Python, making it easy to integrate into your projects.
- Educational Resource: Ideal for students and professionals looking to deepen their understanding of optimization techniques.
Use Cases:
- Machine Learning: Use these algorithms to optimize models in machine learning tasks.
- Data Science: Apply gradient descent methods to improve data analysis and predictive modeling.
- Research: Utilize the implementations for academic research and experimentation in optimization techniques.