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.”

The training dataset consists of 42000 rows each of 784 pixel values thus representing 28 x 28 sized 42000 images of different digits from 0 to 9 . I have used Convolutional Neural Networks to train the model with the help of Keras and made predictions on the 28000 images of the test dataset, also achieved 99.321 % valid accuracy with just 10 epochs . Also tuned ImageDataGenerator to promote generalization and avoid overfitting problem .

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.

The repository implements the a simple Convolutional Neural Network (CNN) from scratch for image classification. I experimented with it on MNIST digits and COIL object dataset.

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.

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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.

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.

MNIST dataset: hand-written digit classification by CNN and SVM. Stock market prediction: LSTM model with feature engineering.

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.

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.

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.

Handwritten Image Classification Model This repository contains a machine learning model designed to classify handwritten images (e.g., digits, letters, or symbols). It leverages Convolutional Neural Networks (CNNs) for accurate and efficient classification. Features: Dataset: Trained on the MNIST ,ensuring robust performance

MNIST dataset is a benchmark in computer vision, containing 70,000 grayscale images of handwritten digits (0–9) sized 28×28 pixels.(CNNs) are highly effective for a task due to their ability to capture spatial hierarchies in image.MNIST serves as a fundamental starting point for beginners to understand image classification & deep learning concepts.

This project applies the Whale Optimization Algorithm (WOA) to tune a compact convolutional neural network for handwritten digit classification on MNIST. The goal is to show how a nature-inspired optimizer can automate CNN hyperparameter search instead of relying on manual trial and error.

This Keras code builds and trains a CNN for MNIST digit classification. The model includes three convolutional layers, each followed by max-pooling, and two dense layers. It achieves 99.00% accuracy on the test set. The dataset is normalized and labels are one-hot encoded.

An introductory deep learning project focused on image classification. This repository implements a multi-layer Convolutional Neural Network (CNN) to classify the 10 handwritten digits (0-9) from the MNIST dataset. The model architecture features Conv2D and MaxPooling layers, successfully achieving a test accuracy of 99.18%

MNIST digit classification using CNN.

MNIST handwritten digit classification CNN model

MNIST Handwritten Digit Classification using CNN

Digit(MNIST) Classification using CNN model.