Deep learning tutorials for classification of MNIST digits using CNNs and solutions to assignments for Udacity's deep learning course

This project implements a Convolutional Neural Network (CNN) to recognize handwritten digits (0–9) using the MNIST dataset. The model is trained on labeled image data, achieving high accuracy in digit classification, and demonstrates the practical application of deep learning techniques in computer vision.”

MNIST Handwritten Digit Classification and Recognition Using Convolutional Neural Network (CNN) Deep Learning

Handwritten Digit Recognition is a deep learning project that uses a Convolutional Neural Network (CNN) to accurately identify digits (0–9) from handwritten images. It leverages the MNIST dataset to train and evaluate the model for real-time digit classification.

Tensorflow and Keras implementation of capsule networks and CNN for the task of image classification using MNIST digits dataset and CIFAR-10.

A collection of Jupyter notebooks containing various MNIST digit and fashion item classification implementations using fully-connected and convolutional neural networks (CNNs) built with TensorFlow and Keras. 2020.

Handwritten digit CNN model for MNIST classification with 99%+ test accuracy. 2 conv layers with max pooling/dropout, fully connected layer with dropout, and softmax output. Trained on MNIST using categorical crossentropy loss and Adam optimizer.

The objective of this project is to recognize handwritten digits based on real time image acquisition from a camera. Currently, several algorithms are suitable for this classification task, such as Bag of Words approach, Decision Trees or Artificial Neural Networks (ANN). However, the most powerful image recognition tools are based on Convolutional Neural Network (CNN), which is going to be the approach chosen for this project. For training and testing MNIST dataset was used. The final net achieved a 98.54% train accuracy and 98.40% test accuracy. For real time segmentation, blob analysis was carried out, and for digit preprocessing, resize and Gauss filter were used to adequate each detected digit to CNN input. The final model was tested in a 1280x720 Webcam with sample time of 1 sec.

Classification of handwritten digits outside of the mnist dataset using a CNN in Keras

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

A android app which classifies the digit by using CNN deep learning model on the basis of MNIST digit classification dataset.

Handwritten Digit Image Classification using CNN (MNIST Dataset) A beginner-friendly Computer Vision project that classifies handwritten digits (0–9) using a Convolutional Neural Network built with TensorFlow and Keras.

In this repository, I have used CNN to MNIST Digit Classification Dataset. You can get insights about Convolutional Neural Network and its implementation for Handwritten Digit Classification.

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.

A Python implementation of a Convolutional Neural Network (CNN) for MNIST digit classification. Features include custom convolution layers, max pooling, ReLU/Tanh activations, and dropout regularization. Built from scratch using NumPy.

MNIST Handwritten Digit Classification using CNN

MNIST digit classification using CNN.

Digit(MNIST) Classification using CNN model.

Use CNN to do handwritten digits classification using MNIST dataset. You can use this notebook as a reference: https://github.com/codebasics/deep-learning-keras-tf-tutorial/blob/main/1_digits_recognition/digits_recognition_neural_network.ipynb

MNIST handwritten digit classification using CNN implemented using Keras

MNIST Handwritten Digit Classification using CNN with TensorFlow/Keras

Image Classification implemented on MNIST (handwritten digits) using Convolutional Neural Networks (CNNs).

Matlab code for MNIST digit classification using Logistic regression, Neural network and CNN

MNIST Digit Recognition using TensorFlow – Train, test, and convert a CNN model to TensorFlow Lite for handwritten digit classification. 🚀

This is a simple example of using Keras_CNN to deal with MNIST(handwritten digit classification) problem.

MNIST dataset classification for Hand-written digit recognition using state-of-the-art CNN algorithms with custom LeNet-5 architecture.

End-to-end machine learning project comparing Logistic Regression, ANN, and CNN for MNIST digit classification, with model evaluation and deployment using a CNN-based API.

A computer vision project demonstrating handwritten digit classification (MNIST) using a from-scratch CNN library (BareCNN) and a classic LeNet-5 architecture.

A simple and interactive MNIST digit classification web app built using Streamlit. Users can draw any digit (0–9) on a canvas, and the app predicts the number using a Convolutional Neural Network (CNN) trained on the MNIST dataset.

The MNIST Handwritten Digits Classification project uses Python, TensorFlow, Keras, and Streamlit to classify 28x28 images of handwritten digits. A trained CNN model predicts digits, and a Streamlit-based frontend allows users to upload images for real-time predictions. This project highlights deep learning's application in image classification