{"id":15132696,"url":"https://github.com/abhinavsharma07/neural-network-using_numpy","last_synced_at":"2026-01-18T17:33:30.950Z","repository":{"id":256033144,"uuid":"854158035","full_name":"AbhinavSharma07/Neural-Network-Using_NUMPY","owner":"AbhinavSharma07","description":"Introduction to Neural Networks (Create a neural network using Numpy)","archived":false,"fork":false,"pushed_at":"2024-09-14T19:17:38.000Z","size":16641,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-05T21:44:59.073Z","etag":null,"topics":["accuracy","feed-forward","model","neural-networks","numpy","prediction"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/AbhinavSharma07.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","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":"2024-09-08T14:30:08.000Z","updated_at":"2024-09-14T19:17:41.000Z","dependencies_parsed_at":"2024-09-08T17:18:10.785Z","dependency_job_id":"3ca62d48-e998-48dd-9e3f-98b5fc06aee3","html_url":"https://github.com/AbhinavSharma07/Neural-Network-Using_NUMPY","commit_stats":{"total_commits":15,"total_committers":1,"mean_commits":15.0,"dds":0.0,"last_synced_commit":"cd75b91fa693480b5aba0921031018d610644627"},"previous_names":["abhinavsharma07/neural-network-using_numpy"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AbhinavSharma07%2FNeural-Network-Using_NUMPY","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AbhinavSharma07%2FNeural-Network-Using_NUMPY/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AbhinavSharma07%2FNeural-Network-Using_NUMPY/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AbhinavSharma07%2FNeural-Network-Using_NUMPY/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/AbhinavSharma07","download_url":"https://codeload.github.com/AbhinavSharma07/Neural-Network-Using_NUMPY/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247406070,"owners_count":20933802,"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":["accuracy","feed-forward","model","neural-networks","numpy","prediction"],"created_at":"2024-09-26T04:22:21.212Z","updated_at":"2026-01-18T17:33:30.921Z","avatar_url":"https://github.com/AbhinavSharma07.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Neural Network Using Numpy\n\n![MNIST Logo](https://upload.wikimedia.org/wikipedia/commons/2/27/MnistExamples.png)\n\n## Introduction\n\nWelcome to the **Neural Network Using Numpy** project! 🎉 In this assignment, you'll dive into the fascinating world of neural networks by building one from scratch using just **Numpy**. Your goal is to create a network that can classify handwritten digits (0-9) from the MNIST dataset.\n\n## Table of Contents\n- [Introduction](#introduction)\n- [Project Overview](#project-overview)\n- [Sections Covered](#sections-covered)\n- [Requirements](#requirements)\n- [Getting Started](#getting-started)\n- [Model Training and Predictions](#model-training-and-predictions)\n- [Resources](#resources)\n- [Jupyter Notebook Viewer](#jupyter-notebook-viewer)\n\n## Project Overview\n\nIn this project, you will:\n- Implement the essential steps to build a neural network.\n- Perform feedforward operations.\n- Compute loss functions.\n- Execute backpropagation for optimization.\n- Update parameters (weights and biases).\n- Train your model to classify handwritten digits.\n\n## Sections Covered\n\n1. **Data Preparation**: Load and preprocess the MNIST dataset.\n2. **Feedforward**: Implement the forward pass of the network.\n3. **Loss Computation**: Calculate the loss to measure the accuracy of the network.\n4. **Backpropagation**: Compute gradients to optimize the network.\n5. **Parameter Updates**: Adjust the weights and biases to improve model performance.\n6. **Model Training and Predictions**: Train the neural network and make predictions on new data.\n\n## Requirements\n\nBefore you begin, ensure you have the following installed:\n\n- Python 3.x\n- Numpy\n- Matplotlib (optional, for visualization)\n\n## Getting Started\n\nTo get started with the project:\n\n1. Clone the repository:\n    ```bash\n    git clone https://github.com/AbhinavSharma07/Neural-Network-Using_NUMPY.git\n    ```\n2. Navigate to the project directory:\n    ```bash\n    cd Neural-Network-using-Numpy\n    ```\n3. Install the required dependencies:\n    ```bash\n    pip install -r requirements.txt\n    ```\n4. Load the dataset:\n   - You can refer to the `mnist.pkl.gz` file for the dataset.\n   - The file will be automatically loaded in the Jupyter notebook.\n\n5. Open the Jupyter Notebook to start coding:\n    ```bash\n    jupyter notebook Neural-Network-using-Numpy.ipynb\n    ```\n\n## Model Training and Predictions\n\nThe notebook is designed to guide you through the entire process of building and training a neural network. By the end of this project, you'll have a fully functional model capable of recognizing handwritten digits with high accuracy.\n\n## Resources\n\n- **MNIST Dataset**: The classic dataset for handwritten digit recognition.\n- **Numpy Documentation**: [Numpy Official Documentation](https://numpy.org/doc/).\n- **Neural Networks**: Understanding how neural networks work and their applications.\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fabhinavsharma07%2Fneural-network-using_numpy","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fabhinavsharma07%2Fneural-network-using_numpy","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fabhinavsharma07%2Fneural-network-using_numpy/lists"}