Build an accurate digit recognition model using PyTorch. Train a deep learning CNN on the MNIST dataset to classify handwritten digits. GPU acceleration for faster training. Ideal for image recognition enthusiasts.

Explore the use of PyTorch and Convolutional Neural Networks (CNNs) for image classification tasks, with a particular focus on handwritten digit recognition.

A Convolutional Neural Network (CNN) based intelligent application for handwritten digit recognition using MNIST dataset, achieving 99.15% test accuracy with PyTorch.

A clean PyTorch implementation of a Convolutional Neural Network (CNN) for handwritten digit recognition using the MNIST dataset, with educational-oriented version.

Handwritten Digit Recognition using CNN A deep learning project that classifies handwritten digits using a Convolutional Neural Network (CNN) trained on the MNIST dataset. Built with PyTorch and deployed using Streamlit for real-time prediction.

EN3150 – Pattern Recognition | University of Moratuwa A PyTorch-based Convolutional Neural Network (CNN) project for handwritten digit recognition using the MNIST dataset. Includes optimizer comparison (Adam, SGD, SGD+Momentum), momentum analysis, and transfer learning with pretrained ResNet18 and VGG16 models.

This project implements handwritten digit recognition on the MNIST dataset using a Convolutional Neural Network (CNN) built with PyTorch. The model is trained with an 80/20 train-validation split and evaluated on the test set, achieving ~97% accuracy.

This project implements a Convolutional Neural Network (CNN) using PyTorch to perform handwritten digit recognition on the MNIST dataset. The model is trained to classify images of digits (0–9) with high accuracy by learning spatial features through convolutional and pooling layers. Training and validation performance are visualized using loss and

Implemented a convolutional neural network (CNN) using TensorFlow and PyTorch for handwritten digit recognition on the MNIST dataset. Preprocessed data including grayscale conversion, resizing, and pixel normalization. Achieved 90% accuracy with the LeNet-5 architecture, optimized using the Adam optimizer.