IJCNN 2015 Hierarchical extreme learning machine for unsupervised representation learning
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Updated
Mar 22, 2019 - MATLAB
IJCNN 2015 Hierarchical extreme learning machine for unsupervised representation learning
Graph Agglomerative Clustering Library
Training an artificial neural network using back-propagation on MNIST dataset
Numerical illustration of a novel analysis framework for consensus-based optimization (CBO) and numerical experiments demonstrating the practicability of the method
Classify the MNIST data by LIBSVM in Matlab.
The purpose of this project is to take handwritten digits as input, process the digits, train the neural network algorithm with the processed data, to recognize the pattern and successfully identify the test digits. The popular MNIST dataset is used for the training and testing purposes. The IDE used is MATLAB
Neural nets for high accuracy multivariable nonlinear regression.
This project contains example of Matlab interface for Caffe (known as Matcaffe). Currently, the example includes
Classificador MNIST
Uses vanilla backpropagation to train a basic multi-layer network to classify digits
A Machine Learning project that uses EM and Bernoulli mixes to classify digits
This repository contains the code to reproduce all of the results in our paper: Making Learners (More) Monotone, T J Viering, A Mey, M Loog, IDA 2020.
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.
Verification of a VAE and SegNet using NNV
Temele la Metode Numerice
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