🚀 Gradient Descent From Scratch

Welcome to the Gradient Descent From Scratch project! This project involves implementing the gradient descent algorithm from scratch to understand its underlying mechanics and how it helps in setting the correct values for neural network parameters. The implementation is demonstrated in a Jupyter Notebook named Gradient Descent From Scratch.ipynb.

📚 Table of Contents

• 📋 Introduction
• 🧠 Understanding Gradient Descent
• 🛠️ Installation
• 🚀 Usage
• 📬 Contact
• 📜 License

📋 Introduction

Gradient descent is a technique that helps us set the correct values for neural network parameters. Without gradient descent, networks wouldn't be able to learn how to make predictions from data. This project implements gradient descent from scratch to provide a clear understanding of how it works.

🧠 Understanding Gradient Descent

Gradient descent is an optimization algorithm used for minimizing the cost function in machine learning models. By iteratively adjusting the parameters in the direction of the negative gradient, the algorithm converges to the minimum of the cost function, thereby improving the model's predictions.

🛠️ Installation

1. Clone the repository:

   git clone https://github.com/syed-muqtasid-ali/gradient-descent-from-scratch.git

2. Navigate to the project directory:

   cd gradient-descent-from-scratch

3. Install the required dependencies:

   pip install -r requirements.txt

🚀 Usage

1. Ensure you have the necessary dependencies installed (see Installation section).

2. Open the Jupyter Notebook:

   jupyter notebook Gradient Descent From Scratch.ipynb

3. Follow the instructions within the notebook to understand and implement gradient descent from scratch.

📬 Contact

For any questions or inquiries, please feel free to contact me via LinkedIn:

📜 License

This project is licensed under the MIT License. See the LICENSE file for details.

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Happy Learning! 🎉