Module 7 Minist Classification using PyTorch | PyTorch | Data Science Advanced Tools

Welcome to Module 7, where we embark on an exciting journey into MNIST classification using Convolutional Neural Networks (CNNs). CNNs are tailored for image input, featuring convolutional layers for feature extraction and pooling layers to distill the most crucial elements.

Learn how CNNs excel in image classification, with a focus on the MNIST handwritten digit classification task. Tackle the challenge of classifying tens of thousands of handwritten digits into numbers ranging from 0 to 9.

We leverage the torchvision API's convenience function to download and load the MNIST dataset directly. Explore the process of loading the dataset, preparing the images, and scaling pixel values for optimal deep learning performance.

Uncover all the essential steps involved, from importing modules and defining the model to forward propagation, dataset preparation, training, and model evaluation.

Join us in this hands-on journey into CNNs and MNIST classification. Subscribe now for practical examples and deep learning insights.