A tutorial for MNIST handwritten digit classification using sklearn, PyTorch and Keras.

Example code to train a Graph Neural Network on the MNIST dataset in PyTorch for Digit Classification

A complete solution for the MNIST handwritten digit classification challenge using PyTorch, including data exploration, model training, and Kaggle submission generation.

Simple MNIST Handwritten Digit Classification using Pytorch

This project uses pyside6 to package a QT program for pytorch-based MNIST handwritten digit classification, which is suitable for demonstrating convolutional neural networks in a cognitive course

Comparison between TensorFlow and PyTorch by the case of MNIST digit classification.

MNIST handwritten digit classification using PyTorch

MNIST digit classification task using PyTorch

MNIST Digit Classification in PyTorch, Lightning & Ignite

"An interactive PyTorch-based solution for MNIST digit classification, featuring Gradio for real-time predictions and customizable model training."

Neural networks from scratch in NumPy, than using PyTorch, progressing from a single logistic neuron to a 2-layer MLP for binary classification and finally an MNIST digit classifier. Each notebook comes with a PDF explaining the math and the intuition behind the code,

Spiking Neural Network (SNN) for MNIST digit image classification using PyTorch

Neural networks implemented from scratch (NumPy) and in PyTorch for MNIST digit and Sign Language image classification.

I have build 2 different models on Pytorch to classify same data set. The first one based on Deep Neural Network and second on Convolutional Neural Networks.

use CNN based on AlexNet to implement the classification of handwriting digits (not MNIST) - pytorch

This project demonstrates a handwritten digit classification pipeline using the classic MNIST dataset. It leverages scikit-learn, PyTorch, and Skorch to build and evaluate a neural network for classifying handwritten digits (0–9).

This project implements a CNN for handwritten digit classification on the MNIST dataset using PyTorch. It uses stacked convolutional layers with dropout, batch normalization, and max pooling to classify 28×28 grayscale digits (0–9) with Softmax output.

Three PyTorch ML Projects: 1. MNIST Digit Classification (CNN) 2. IMDB Sentiment Analysis (Text Embedding + Feedforward) 3. California Housing Price Prediction (Regression NN). All scripts support GPU, include training, saving models.

Classification of MNIST Handwritten Digits using pytorch

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In this project, we’re teaching a computer to recognize hand-written numbers (digits) using an advanced method called a Convolutional Neural Network (CNN). The data we are working with is the MNIST dataset, which consists of 28x28 pixel images of hand-written digits, from 0 to 9.

A pytorch implementation of a convolutional network on the MNIST digit classification dataset

Digit classification using MNIST dataset and a Multiperceptron Network(MLP) defined and trained in pytorch

MNIST Handwritten Digit Classification using PySpark and PyTorch: A Big Data and Deep Learning Approach

PyTorch Neural Network for MNIST digit classification achieving over 95% accuracy. Includes training, testing, and performance visualization.

A PyTorch-based CNN for MNIST digit recognition, featuring data augmentation, a Streamlit app, and real-time handwriting classification.

Hybrid Quantum-Classical Neural Network for MNIST digit classification using Qiskit and PyTorch. Features optimized training configurations, gradient clipping, and comprehensive visualization tools.

A biologically-inspired spiking CNN built with snnTorch and PyTorch for MNIST digit classification, simulating Leaky Integrate-and-Fire neurons over time with surrogate gradient learning.