Ioannis Kourouklides's repositories
awesome-machine-learning
A curated list of awesome Machine Learning frameworks, libraries and software.
Coursera-Machine-Learning
source from exercises in Coursera.
perspective-taking
Visual and Spatial Perceptual Perspective Taking (using Kinect)
DeepLearnToolbox
Matlab/Octave toolbox for deep learning. Includes Deep Belief Nets, Stacked Autoencoders, Convolutional Neural Nets, Convolutional Autoencoders and vanilla Neural Nets. Each method has examples to get you started.
KDT-toolbox
A MATLAB Toolbox for Kernel Dynamic Textures
NPBayesHMM
Nonparametric Bayesian Inference for Sequential Data. Includes state-of-the-art MCMC inference for Beta process Hidden Markov Models (BP-HMM). Implemented in Matlab.
Astro-Fuel
NASA Space Apps Challenge 2016 : ASTEROID MINING
AzureML-Regression-Example
This repo contains all the code and data necessary to explore non-linear regression using Azure ML.
belote-ai
This program is a belote card game that allow 4 artificial intelligences to play against each other
bnt
Bayes Net Toolbox for Matlab
ConvNet
Convolutional Neural Networks for Matlab. Has versions for GPU and CPU, written on CUDA, C++ and Matlab. All versions work identically. The GPU version uses kernels from Alex Krizhevsky's library 'cuda-convnet2'.
deepmat
Matlab Code for Restricted/Deep Boltzmann Machines and Autoencoders
HOG-Pedestrian-Detector
MATLAB implementation of a basic HOG + SVM pedestrian detector.
kaggle-blackbox
Deep learning made easy
logic
A logic programming library for F#
matconvnet
MatConvNet: CNNs for MATLAB
mmcm-net
C# framework for developing large hierarchical models of self organizing neural maps. Developed by stephane-lallee, exported to GitHub for archival purposes.
practical-object-category-detection
A VGG practical on the detection of visual object categories
seldon-server
Serves predictions via a REST API
Stanford-Machine-Learning-Course
machine learning course programming exercise
Stanford.NLP.NET
Stanford NLP for .NET
stanford_dl_ex
Programming exercises for the Stanford Unsupervised Feature Learning and Deep Learning Tutorial
TensorFlow-VAE-GAN-DRAW
A collection of generative methods implemented with TensorFlow (Deep Convolutional Generative Adversarial Networks (DCGAN), Variational Autoencoder (VAE) and DRAW: A Recurrent Neural Network For Image Generation).