https://github.com/udacity-machinelearning-internship/mini-batch-gradient-descent
Implementing mini batch gradient descent using sckit-learn
https://github.com/udacity-machinelearning-internship/mini-batch-gradient-descent
machine-learning mini-batch-gradient-descent sckiit-learn
Last synced: 3 months ago
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Implementing mini batch gradient descent using sckit-learn
- Host: GitHub
- URL: https://github.com/udacity-machinelearning-internship/mini-batch-gradient-descent
- Owner: Udacity-MachineLearning-Internship
- Created: 2024-05-14T14:14:33.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-05-17T03:43:45.000Z (about 1 year ago)
- Last Synced: 2025-01-21T08:24:10.138Z (5 months ago)
- Topics: machine-learning, mini-batch-gradient-descent, sckiit-learn
- Language: Jupyter Notebook
- Homepage:
- Size: 83 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
  [](https://www.python.org/downloads/release/python-380/)
[](https://pypi.org/project/pip/21.0/)
[](https://github.com/BaraSedih11/mini-batch-gradient-descent/releases/tag/v1.0.0)
[](https://github.com/BaraSedih11/mini-batch-gradient-descent/graphs/contributors)
This repository contains an implementation of Mini-Batch Gradient Descent, a variant of the Gradient Descent optimization algorithm often used in machine learning and deep learning. Mini-Batch Gradient Descent is particularly useful when dealing with large datasets, as it updates the model's parameters using a subset of the training data at each iteration.
## Overview
Gradient Descent is a popular optimization algorithm used to minimize the loss function in machine learning models by iteratively moving towards the minimum of the loss function. Mini-Batch Gradient Descent is a variation of this algorithm where instead of computing the gradient of the entire dataset (Batch Gradient Descent) or just one sample (Stochastic Gradient Descent), it computes the gradient based on a randomly selected subset of the training data (mini-batch). This approach combines the advantages of both Batch Gradient Descent and Stochastic Gradient Descent, making it suitable for large-scale datasets.
This repository provides a simple implementation of Mini-Batch Gradient Descent in Python, along with examples demonstrating its usage.
## Contents
- `Mini-Batch Gradient Descent.ipynb`: Jupyter Notebook containing the implementation of polynomial regression using Python.
- `data.csv`: Sample dataset used in the notebook for demonstration purposes.
- `README.md`: This file providing an overview of the repository.
## Requirements
To run the code in the Jupyter Notebook, you need to have Python installed on your system along with the following libraries:
* NumPy
* pandas
* scikit-learn
* matplotlib
You can install these libraries using pip:
```bash
pip install numpy pandas scikit-learn matplotlib
```
## Usage
1. Clone this repository to your local machine:
```bash
git clone https://github.com/BaraSedih11/Mini-Batch-Gradient-Descent.git
```
2. Navigate to the repository directory:
```bash
cd Mini-Batch Gradient Descent
```
3. Open and run the Jupyter Notebook `Mini-Batch Gradient Descent.ipynb` using Jupyter Notebook or JupyterLab.
4. Follow along with the code and comments in the notebook to understand how mini-batch gradient descent is implemented using Python.
## Acknowledgements
- [scikit-learn](https://scikit-learn.org/): The scikit-learn library for machine learning in Python.
- [NumPy](https://numpy.org/): The NumPy library for numerical computing in Python.
- [pandas](https://pandas.pydata.org/): The pandas library for data manipulation and analysis in Python.
- [matplotlib](https://matplotlib.org/): The matplotlib library for data visualization in Python.