This project involves building a simple neural network to classify handwritten digits using the MNIST dataset, a well-known dataset in computer vision. The model aims to achieve high accuracy in recognizing digits, with your neural network reaching an accuracy of 96%

This project is a neural network-based classifier that can identify handwritten digits with high accuracy. Using the MNIST dataset, it provides a great starting point for computer vision and deep learning projects.

• Source: MNIST Database
• Size: 60,000 training images, 10,000 test images, each 28x28 pixels.

The goal is to correctly classify handwritten digit images (0-9) by building and evaluating a neural network model.

• Normalized pixel values for faster convergence.
• Reshaped the data to match input requirements of the neural network.

• Developed a simple neural network for classification.
• Trained the model using the training dataset, achieving 96% accuracy on the test dataset.

• Assessed the model performance using accuracy and visualized results with sample predictions.

• The neural network model achieved 96% accuracy on the test dataset.

1. Clone this repository:

   git clone https://github.com/gurramankit/MNIST_DL_Sigmoid.git

2. Install the required libraries:

   pip install -r requirements.txt

3. Run the project:

   python main.py

• data/ - Contains the MNIST dataset.
• notebooks/ - Jupyter notebooks with model building and evaluation.
• src/ - Python scripts for data processing and model implementation.
• README.md - Overview and instructions.
• requirements.txt - Dependencies for running the project.

For a detailed explanation of the approach, refer to my blog post: Your First Deep Learning Project on Medium

For questions or suggestions, feel free to reach out at [ankithkumarankith122@gmail.com].