| CNN | MNIST | Classification |

Convolutional Neural Networks (CNN) with Computer Vision (CV) for MNIST Handwriting Classification

Introduction

This notebook delves into a classification task involving the use of Convolutional Neural Networks (CNNs) on the famous MNIST dataset, available at Yann LeCun's website.

We have structured the notebook into two main sections:

Objectives

This section delineates the specific goals of this notebook, which are:

• Training a Deep Neural Network (DNN) model to achieve high accuracy in recognizing handwritten digits.

Implementation

This section presents the hands-on steps necessary to attain the previously mentioned objectives. These steps include:

1. Imports, Constants, and Methods: Setting up the necessary libraries, constants, and methods for our task.
2. Data Retrieval: Acquiring the MNIST dataset to be used for training and testing purposes.
3. Data Preparation: Preprocessing and setting up the dataset to facilitate effective training of the CNN model.
4. Model Creation: Architecting and constructing the CNN model utilizing Keras.
5. Model Training: Engaging the CNN model in learning using the prepared dataset.
6. Evaluation: Gauging the trained model's performance and analyzing the classification results.

Project:

• Github
• Kaggle