MNIST Digit Recognition using Neural Network

This project implements a simple neural network for recognizing handwritten digits using the MNIST dataset. The model is built using TensorFlow and Keras.

Getting Started

Prerequisites

• Python 3.x
• TensorFlow
• Keras
• Matplotlib

Installation

Follow these steps to set up the MNIST Digit Recognition project on your local machine:

1. Clone the Repository:

   git clone https://github.com/your-username/mnist-digit-recognition.git
   cd mnist-digit-recognition

   This command clones the project repository to your machine and navigates to the project directory.

2. Install Dependencies:

   pip install -r requirements.txt

   This command installs all the required dependencies specified in the requirements.txt file. These dependencies include Python 3.x, TensorFlow, Keras, and Matplotlib.

   Note: It's recommended to use a virtual environment to avoid conflicts with existing Python packages. If you don't have pip installed, you can install it following the instructions here.

3. Explore the Project:

   Now that you have cloned the repository and installed the dependencies, you are ready to explore the project. The main components include the Jupyter Notebook Bootcamp.ipynb, which is used for training the neural network on the MNIST dataset.

   jupyter notebook Bootcamp.ipynb

   Open the notebook in a compatible environment (e.g., Google Colab, Jupyter) and follow the instructions to train the model.

4. Training the Model:

   Within the Jupyter Notebook, execute the provided Python code cells to load and preprocess the MNIST dataset, build and train the neural network, and save the trained model as mnist_model.h5.

5. Testing the Model:

   After training, you can use the trained model for testing by following the instructions in the "Testing the Model" section of the README. This involves loading the model in a separate notebook or script and making predictions on test images.

By following these steps, you'll have the MNIST Digit Recognition project set up on your local machine, ready for exploration and use.

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Outcome

After training the neural network on the MNIST dataset, you can observe the model's performance and make predictions on new test images. Below is an example of the project outcome:

Description: The image above demonstrates the model's prediction on a test image. The left side shows the original image with the true label, while the right side displays the normalized image with the predicted label generated by the trained neural network.

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