{"id":21593632,"url":"https://github.com/Udacity-MachineLearning-Internship/Mini-Batch-Gradient-Descent","last_synced_at":"2025-07-17T01:31:59.141Z","repository":{"id":239774605,"uuid":"800534114","full_name":"BaraSedih11/Mini-Batch-Gradient-Descent","owner":"BaraSedih11","description":"Implementing mini batch gradient descent using sckit-learn","archived":false,"fork":false,"pushed_at":"2024-05-17T03:43:45.000Z","size":83,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2024-05-17T04:37:18.803Z","etag":null,"topics":["machine-learning","mini-batch-gradient-descent","sckiit-learn"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/BaraSedih11.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-05-14T14:14:33.000Z","updated_at":"2024-05-17T03:43:49.000Z","dependencies_parsed_at":"2024-05-17T04:45:24.256Z","dependency_job_id":null,"html_url":"https://github.com/BaraSedih11/Mini-Batch-Gradient-Descent","commit_stats":null,"previous_names":["barasedih11/mini-batch-gradient-descent"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BaraSedih11%2FMini-Batch-Gradient-Descent","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BaraSedih11%2FMini-Batch-Gradient-Descent/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BaraSedih11%2FMini-Batch-Gradient-Descent/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BaraSedih11%2FMini-Batch-Gradient-Descent/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/BaraSedih11","download_url":"https://codeload.github.com/BaraSedih11/Mini-Batch-Gradient-Descent/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":226193696,"owners_count":17588179,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["machine-learning","mini-batch-gradient-descent","sckiit-learn"],"created_at":"2024-11-24T17:13:48.220Z","updated_at":"2025-07-17T01:31:53.855Z","avatar_url":"https://github.com/BaraSedih11.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003cdiv align=center\u003e\n  \n  ![Mini-Batch_Gradient_Descent](https://github.com/BaraSedih11/Mini-Batch-Gradient-Descent/assets/98843912/8fa085fe-8cef-44fd-9553-92cb59b631ac)\n\n\n   ![GitHub repo size](https://img.shields.io/github/repo-size/BaraSedih11/mini-batch-gradient-descent) ![GitHub repo file count (file type)](https://img.shields.io/github/directory-file-count/BaraSedih11/mini-batch-gradient-descent) [![Python Version](https://img.shields.io/badge/python-3.8-blue)](https://www.python.org/downloads/release/python-380/)\n[![Pip Version](https://img.shields.io/badge/pip-21.0-orange)](https://pypi.org/project/pip/21.0/)\n ![GitHub last commit (branch)](https://img.shields.io/github/last-commit/BaraSedih11/mini-batch-gradient-descent/main)\n[![Version](https://img.shields.io/badge/version-v1.0.0-blue)](https://github.com/BaraSedih11/mini-batch-gradient-descent/releases/tag/v1.0.0)\n[![Contributors](https://img.shields.io/github/contributors/BaraSedih11/mini-batch-gradient-descent)](https://github.com/BaraSedih11/mini-batch-gradient-descent/graphs/contributors)\n![GitHub pull requests](https://img.shields.io/github/issues-pr-raw/BaraSedih11/mini-batch-gradient-descent)\n  \n\u003c/div\u003e\n\nThis 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.\n\n## Overview\n\nGradient 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.\n\nThis repository provides a simple implementation of Mini-Batch Gradient Descent in Python, along with examples demonstrating its usage.\n\n## Contents\n\n- `Mini-Batch Gradient Descent.ipynb`: Jupyter Notebook containing the implementation of polynomial regression using Python.\n- `data.csv`: Sample dataset used in the notebook for demonstration purposes.\n- `README.md`: This file providing an overview of the repository.\n\n\n## Requirements\nTo run the code in the Jupyter Notebook, you need to have Python installed on your system along with the following libraries:\n\n* NumPy\n* pandas\n* scikit-learn\n* matplotlib\nYou can install these libraries using pip:\n\n```bash\npip install numpy pandas scikit-learn matplotlib\n```\n\n## Usage\n\n1. Clone this repository to your local machine:\n\n```bash\ngit clone https://github.com/BaraSedih11/Mini-Batch-Gradient-Descent.git\n```\n\n2. Navigate to the repository directory:\n\n```bash\ncd Mini-Batch Gradient Descent\n```\n\n3. Open and run the Jupyter Notebook `Mini-Batch Gradient Descent.ipynb` using Jupyter Notebook or JupyterLab.\n\n4. Follow along with the code and comments in the notebook to understand how mini-batch gradient descent is implemented using Python.\n\n\n## Acknowledgements\n\n- [scikit-learn](https://scikit-learn.org/): The scikit-learn library for machine learning in Python.\n- [NumPy](https://numpy.org/): The NumPy library for numerical computing in Python.\n- [pandas](https://pandas.pydata.org/): The pandas library for data manipulation and analysis in Python.\n- [matplotlib](https://matplotlib.org/): The matplotlib library for data visualization in Python.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FUdacity-MachineLearning-Internship%2FMini-Batch-Gradient-Descent","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FUdacity-MachineLearning-Internship%2FMini-Batch-Gradient-Descent","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FUdacity-MachineLearning-Internship%2FMini-Batch-Gradient-Descent/lists"}