Beginner’s guide to AI with digit recognition using MNIST dataset #MNIST

MNIST: A Beginner’s Guide to Artificial Intelligence with Digit Recognition | by Islam Kassem | Jul, 2024

The MNIST dataset is widely used in the machine learning community for its simplicity and learning opportunities. It consists of 60,000 training images and 10,000 test images, each 28×28 pixels with grayscale values from 0 to 255. The dataset is provided in two CSV files: train.csv with label and pixel values, and test.csv without labels. The goal is to predict the digit in each test image for the Digit Recognizer competition on Kaggle.

To build a model, a Convolutional Neural Network (CNN) is chosen for its effectiveness in image recognition. The process involves data preprocessing, model architecture design with convolutional, pooling, and dense layers, training the model, and evaluating its performance. The model is compiled with the Adam optimizer and categorical cross-entropy loss.

The model is trained using the training data and then used to make predictions on the test data. The predictions are converted to class labels and saved in a CSV file for submission. The model’s architecture and summary are visualized, showcasing the layers and parameters. The results of the model predictions on test images are displayed, demonstrating high accuracy in recognizing handwritten digits.

Overall, working with the MNIST dataset and building a CNN model for the Digit Recognizer competition has been a valuable learning experience in artificial intelligence and computer vision. The project serves as an introduction to these fields, with potential for further exploration and development in the future.

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