Deep Learning with MNIST Dataset

This repository contains a simple example of deep learning using the MNIST dataset with TensorFlow/Keras.

Overview

The MNIST dataset is a collection of 28x28 pixel grayscale images of handwritten digits (0 through 9). In this project, we build and train a neural network to recognize and classify these digits.

Getting Started

Prerequisites

Make sure you have Python and TensorFlow installed. You can install the required packages using the following:

pip install tensorflow matplotlib

Dataset

The MNIST dataset is automatically downloaded by TensorFlow when running the code.

Code Structure

mnist_deep_learning.py: Python script containing the code for building, training, and testing the neural network. Usage Run the simple_image_recognition.ipynb script to train the model and make predictions on sample images.

jupyter nbconvert --execute simple_image_recognition.ipyng

Results

The model's accuracy and predictions on sample images will be displayed.

Author

Gratus Richard A

License

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