This is a DCGAN trained on MNIST model. It has all the specifications as described the original paper on Deep Convolutional General Adversarial Training

VAE Implementation with LSTM Encoder and CNN Decoder

Simple NN for MNIST Recognition

A Convolutional neural network heavily based upon the tensorflow advanced MNIST example but equiped with labels to visualize and allowing the user to draw an image and then have the system predict the result.

CNN model for predicting random input.

All of the code developed as part of my learning experience in the programming language R

Digit Recognition on MNIST Data

Trained deep neural networks to predict and classify input image (MNISTDD) datasets with python.

Dockerize a Keras CNN model, which is wrapped in a Webapp using Flask Micro Framework

MNIST Digits Classification with numpy only

Study of leNet implementation in Python3.6 with Keras+Tensorflow backend.

PyTorch implementation of a feed forward neural network to classify handwritten digits from the MNIST dataset

Deep learning demos using MNIST data set with multiple neural network models

Deep Neural Networks like Single Layer Perceptron and Multi Layer Perceptron implementation using Tensorflow library on Datasets like MNIST and Naval Mine for categorical Classification. Saving and Restoring Tensorflow "Variables" weights for testing.

Performs OCR on the MNIST dataset. From my BSc. AI & Robotics at Prifysgol Aberystwyth

Building a model to recognise handwritten numerical digits from images of the MNIST dataset.

Problems Identification: This project involves the implementation of efficient and effective KNN classifiers on MNIST data set. The MNIST data comprises of digital images of several digits ranging from 0 to 9. Each image is 28 x 28 pixels. Thus, the data set has 10 levels of classes.