This repository contains a simple example of deep learning using the MNIST dataset with TensorFlow/Keras.
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.
Make sure you have Python and TensorFlow installed. You can install the required packages using the following:
pip install tensorflow matplotlib
The MNIST dataset is automatically downloaded by TensorFlow when running the code.
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
The model's accuracy and predictions on sample images will be displayed.
This project is licensed under the MIT License - see the LICENSE file for details.