Performs OCR on the MNIST dataset. From my BSc. AI & Robotics at Prifysgol Aberystwyth
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Updated
Jul 10, 2015 - R
Performs OCR on the MNIST dataset. From my BSc. AI & Robotics at Prifysgol Aberystwyth
A collection of codes for 'how far can we go with MNIST' challenge
Deep learning demos using MNIST data set with multiple neural network models
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.
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.
I implemented a Naive Bayes classifier form scratch and applied it on MNIST dataset.
Simple NN for MNIST Recognition
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.
Study of leNet implementation in Python3.6 with Keras+Tensorflow backend.
Building a model to recognise handwritten numerical digits from images of the MNIST dataset.
Played with Tensorspace a library for Neural network 3D visualization, building interactive and intuitive models in browsers, supports pre-trained deep learning models from TensorFlow, Keras, TensorFlow.js
Dockerize a Keras CNN model, which is wrapped in a Webapp using Flask Micro Framework
Some basic implementations of Variational Autoencoders in pytorch
Digit Recognition on MNIST Data
MNIST Digits Classification with numpy only
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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