CNN with PyTorch for MNIST Digit Classification from scratch| Hands-on Machine Learning with PyTorch

In this hands-on coding session, we dive deep into building a Convolutional Neural Network (CNN) from scratch using PyTorch to classify handwritten digits from the MNIST dataset. We start by exploring the dataset with essential Exploratory Data Analysis (EDA) techniques, visualize class distributions and sample images, and explain the role of data transformations and dataloaders in PyTorch. Next, we walk through every part of the CNN architecture — from convolutional layers to fully connected layers — explaining why each component is essential for feature extraction and classification. We then define our loss function using CrossEntropyLoss, choose an optimizer, and train the model while tracking the training loss over epochs. After training, we evaluate the model with a classification report and a confusion matrix, helping you understand precision, recall, and F1-score in a multi-class setting. Finally, we demonstrate how to perform inference on a single image and interpret the prediction. This tutorial is ideal for beginners and intermediate learners who want to understand how CNNs work internally and how to implement them step-by-step using PyTorch — without relying on high-level abstractions. By the end of this video, you’ll have a complete working model and the confidence to experiment further with your own CNN-based projects. 🔔 Don’t forget to subscribe and turn on notifications to follow the rest of the Deep Learning Series!