SyedMuqtasidAli / Fully-Connected-Dense-Layer

This project is about MNIST Handwritten Digit Single-Label Multi-class Classification problem (Densely Connected Network)

MNIST Handwritten Digit Classification 🔢

This project is about the MNIST Handwritten Digit Single-Label Multi-class Classification problem using a densely connected neural network.

Table of Contents 📑

• Introduction 📘
• Features ✨
• Installation ⚙️
• Usage 🚀
• Contact 📬
• License 📜

Introduction 📘

This project focuses on classifying handwritten digits from the MNIST dataset. It utilizes a densely connected neural network to perform single-label multi-class classification, accurately identifying digits from 0 to 9.

Features ✨

• Model Architecture: Densely connected neural network.
• Training and Evaluation: Scripts for training and evaluating the model.
• Visualization: Tools for visualizing the model's predictions and performance metrics.

Installation ⚙️

1. Clone the repository:

   git clone https://github.com/yourusername/mnist-handwritten-digit-classification.git

2. Navigate to the project directory:

   cd mnist-handwritten-digit-classification

3. Install the required dependencies:

   pip install -r requirements.txt

Usage 🚀

To run the Jupyter Notebook and start training the model, follow these steps:

1. Ensure you have Jupyter installed. If not, install it using:

   pip install jupyter

2. Open the Jupyter Notebook:

   jupyter notebook Untitled9.ipynb

3. Follow the instructions within the notebook to load data, train the model, and evaluate its performance.

Contact 📬

Feel free to contact me on LinkedIn for any questions or collaborations:

License 📜

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