Gradient Descent Algorithm This repository contains an implementation of the Gradient Descent algorithm in Python. The Gradient Descent algorithm is a popular optimization technique used in machine learning and other fields to find the minimum of a function.
Introduction Gradient Descent is an iterative optimization algorithm used to find the minimum of a function. It is commonly used in machine learning to update the parameters of a model in order to minimize a cost function. The algorithm works by iteratively adjusting the parameters in the direction of steepest descent of the cost function.
This repository provides a Python implementation of the Gradient Descent algorithm, which can be easily integrated into your own projects or used as a standalone tool for optimization tasks.
Usage To use the Gradient Descent algorithm, follow these steps:
Install the required dependencies (see Installation). Import the gradient_descent module into your Python script or use the provided example code. Define your target function that you want to minimize. Specify the initial parameter values and the learning rate. Run the algorithm for a certain number of iterations or until convergence is reached. The optimized parameter values will be updated during each iteration.
Installation To use the Gradient Descent algorithm, you need to have Python 3 installed on your system. There are no additional dependencies required.
Clone this repository to your local machine or download the source code. Navigate to the repository directory. You can now run the code using your preferred Python environment.
Contributing Contributions to this repository are always welcome. If you find any issues or have suggestions for improvements, please open an issue or submit a pull request.