Update README.md

README.md

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-## SET 1 - MICROGRAD 🔗
-
-[![Documentation](https://img.shields.io/badge/Documentation-Available-blue)](https://muzzammilshah.github.io/Road-to-GPT/Micrograd/)
-![Number of Commits](https://img.shields.io/github/commit-activity/m/MuzzammilShah/NeuralNetworks-Micrograd-Implementation?label=Commits)
-[![Last Commit](https://img.shields.io/github/last-commit/MuzzammilShah/NeuralNetworks-Micrograd-Implementation.svg?style=flat)](https://github.com/MuzzammilShah/NeuralNetworks-Micrograd-Implementation/commits/main)  
-![Project Status](https://img.shields.io/badge/Status-Done-success)
-
- 
-
-### **Overview**
-This repository contains the implementation of **Backpropagation** using an **AutoGrad Engine**, inspired by the **Micrograd** video by Andrej Karpathy. It explores the foundations of training neural networks and implementing key operations from scratch.
-
-The repository contains:
-
-- **Manual Backpropagation**: Building intuition and understanding of the gradient calculation process.
-- **Interactive Site Version**: A pilot version of an interactive site that visualizes the functionality, currently under development.
-
-✍🏻 Notes: Follow the notebooks in order for a structured learning path. Each notebook and note corresponds to a particular concept or milestone in the implementation.
-
- 
-
-### **🗂️Repository Structure**
-
-```plaintext
-├── .gitignore
-├── README.md
-├── notes/
-│   ├── A-main-video-lecture-notes.md
-│   ├── chatgpt-motivation.md
-│   ├── crux-node-backpropagation.md
-│   ├── expanding-tanh-and-adding-more-operations.md
-│   ├── micrograd-functionality.md
-│   ├── multi-layer-perceptron.md
-│   ├── neurons-explanation.md
-│   ├── pytorch-comparision.md
-│   └── value-object-creation.md
-├── site/
-│   ├── interactive_site_pilot_v1.2/
-├── 1-derivative-simple-function.ipynb
-├── 2-derivative-function-with-multiple-inputs.ipynb
-├── 3-value-object.ipynb
-├── 3_1-graph-visualisation.ipynb
-├── 4_0-manual-backpropagation_simpleExpression.ipynb
-├── ... (more implementation notebooks, there are a lot lol)
-```
-
-- **Notes Directory**: Contains Markdown files with notes and explanations for each topic.
-- **Interactive Site Directory**: Contains files for the pilot version of the interactive visualization tool.
-- **Implementation Notebooks**: Step-by-step code for implementing and understanding backpropagation and related concepts.
-
- 
-
-### **📄Instructions**
-
-1. Start by reading the notes in the `notes/` directory for a theoretical understanding.
-2. Proceed with the notebooks in the root directory in order to build up the implementation step by step.
-3. Explore the `site/` directory for the pilot interactive version of the AutoGrad Engine visualization (Idea concept, not yet implemented)
-
- 
-
-### **⭐Documentation**
-
-For a better reading experience and detailed notes, visit my **[Road to GPT Documentation Site](https://muzzammilshah.github.io/Road-to-GPT/Micrograd/)**. 
-
-> **💡Pro Tip**: This site provides an interactive and visually rich explanation of the notes and code. It is highly recommended you view this project from there.
-
- 
-
-### **✍🏻Acknowledgments**
-Notes and implementations inspired by the **Micrograd** video by [Andrej Karpathy](https://karpathy.ai/).  
-
+---
+license: mit
+datasets: []
+language:
+- en
+model_name: Micrograd AutoGrad Engine
+library_name: pytorch
+tags:
+- micrograd
+- autograd
+- backpropagation
+- neural-networks
+- andrej-karpathy
+---
+
+# Micrograd AutoGrad Engine: Backpropagation Implementation
+
+This repository contains the implementation of **Backpropagation** using an **AutoGrad Engine**, inspired by the **Micrograd** video by Andrej Karpathy. It explores the foundations of training neural networks and implementing key operations from scratch.
+
+## Overview
+- **Manual Backpropagation**: Building intuition and understanding of the gradient calculation process.
+- **Implementation Notebooks**: Step-by-step code for implementing and understanding backpropagation and related concepts.
+
+## Documentation
+For a better reading experience and detailed notes, visit my **[Road to GPT Documentation Site](https://muzzammilshah.github.io/Road-to-GPT/Micrograd/)**.
+
+> **💡 Pro Tip**: This site provides an interactive and visually rich explanation of the notes and code. It is highly recommended you view this project from there.
+
+## Acknowledgments
+Notes and implementations inspired by the **Micrograd** video by [Andrej Karpathy](https://karpathy.ai/).
+
 For more of my projects, visit my [Portfolio Site](https://muhammedshah.com).