A Neural Network implemented from scratch as per http://neuralnetworksanddeeplearning.com/ in Rust. This is then trained on MNIST. This is also used in Rust-NN-Web project to compile to WASM and recognize digits.
Capsule Network implementation in Tensorflow
Quantum MNIST using amplitude encoding instead of dimensionality reduction.
Java read Tensorflow model created at Deep MNIST for Experts
"THE MNIST DATABASE of handwritten digits" DataReader http://yann.lecun.com/exdb/mnist/
Improved implementation of Tariq Rashid's "Make Your Own Neural Network"
Child repository of Deep-learning-with-PyTorch-and-GCP
MNIST handwritten digit classification using PyTorch
Convert data in IDX format in MNIST Dataset to Numpy Array using Python
Implementation of the original VAT (Virtual Adversarial Training) paper in Keras.
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.
Child repository of Deep-learning-with-PyTorch-and-GCP
This is a Java implementation of a simple 3-layer neural network that interprets handwritten digits.
This repository contains the implementation and visualization of some autoencoders for latent space pattern-learning
An american sign language recognition task using CNN and machine learning algorithms
Handwritten digit recognition system trained on MNIST data.
Assignment submissions for CSCI 5622 at CU Boulder
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