About this Project

This repository contains a small Convolutional Neural Network (CNN) implemented to work with the MNIST database. The MNIST database is a widely used dataset consisting of 70,000 images of handwritten digits, making it an excellent resource for learning and experimenting with image recognition and deep learning models.

• Core Functionality: Implements a Convolutional Neural Network (CNN) for image classification tasks.

• Dataset: Utilizes the MNIST database, which contains 70,000 images of handwritten digits (0-9).

• Technology: Primarily developed using Python and likely leverages libraries common in machine learning and deep learning, such as TensorFlow or PyTorch, and data manipulation libraries like NumPy. The project is presented in a Jupyter Notebook format, facilitating interactive development and visualization.

• Purpose: Serves as an educational tool or a foundational example for understanding CNNs and their application in recognizing handwritten digits.

This project is a great starting point for anyone interested in diving into the world of neural networks and computer vision.