This is a DCGAN trained on MNIST model. It has all the specifications as described the original paper on Deep Convolutional General Adversarial Training
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Updated
Jul 25, 2020 - Jupyter Notebook
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.
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
classification of handwritten digits from MNIST dataset with above 99% accuracy using simple CNNs
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.
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