Ioannis Kourouklides

artificial_neural_networks. A collection of Methods and Models for various architectures of Artificial Neural Networks

codility-csharp. Solutions to Codility tests in C#

codility-python. Solutions to Codility tests in Python

CEI_523_2018. CEI 523: Data Science - Fall 2018

fsharp-examples. Example code in .NET F#

Biography. Biography of Ioannis Kourouklides

perspective-taking. Visual and Spatial Perceptual Perspective Taking (using Kinect)

codility_training. Solutions for codility training assignments in python from http://codility.com/train/

Publications. List of publications

bayesian_uncertainty_adversaries. Thesis: Detecting Adversaries in DQNs and Computer Vision using Bayesian CNNs

python-examples. Example code in Python

bayesian-drl. Bayesian Deep Reinforcement Learning research repository

csharp-examples. Example code in .NET C#

website. Provisional draft of the website

Coursera-Machine-Learning. source from exercises in Coursera.

hypercl. Continual Learning with Hypernetworks. A continual learning approach that has the flexibility to learn a dedicated set of parameters, fine-tuned for every task, that doesn't require an increase in the number of trainable weights and is robust against catastrophic forgetting.

Bayesian-Compression-for-Deep-Learning. Remplementation of paper https://arxiv.org/abs/1705.08665

av_hubert. A self-supervised learning framework for audio-visual speech

awesome-machine-learning. A curated list of awesome Machine Learning frameworks, libraries and software.

SRNN-Brain-Modelling-Toolbox. Spatiotemporal Dynamics in Spiking Recurrent Neural Networks using Optimization-based Modelling for EEG signals

hypnettorch. Package for working with hypernetworks in PyTorch.

practicalAI. 📚A practical approach to learning machine learning.

PyDataCyprus. PyData Cyprus, meetup minutes (e.g: slides, datasets, code etc)

belot. Card game with reinforcement learning policy-based agent implementation

Automatic-Bridge-Bidding-by-Deep-Reinforcement-Learning. The released model of the paper 'Automatic Bridge Bidding by Deep Reinforcement Learning' in ECAI 2016

yapf. A formatter for Python files

vae. Variational Autoencoder / Deep Latent Gaussian Model demo

Talks. List of talks

keras. Deep Learning for humans

kaggle. My public Kaggle code

tensor2tensor. Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.

deep-rl. Collection of Deep Reinforcement Learning algorithms

scala-examples. Example code in Scala

Tutorial-SoftWeightSharingForNNCompression. A tutorial on 'Soft weight-sharing for Neural Network compression' published at ICLR2017

pyspark. pyspark

CEI_523_2019. CEI 523: Data Science - Fall 2019

models. Models and examples built with TensorFlow

learning-to-communicate-pytorch. Learning to Communicate with Deep Multi-Agent Reinforcement Learning in PyTorch

deep-learning-book. Repository for "Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python"

pilotta-1. Python

reinforcement-learning-examples. Implementation of examples & exercises from the Sutton & Barto book Reinforcement Learning An Introduction

nlp_classification. PyData Tel Aviv NLP Workshop

TensorFlow-Tutorials. TensorFlow Tutorials with YouTube Videos

VAE-TensorFlow. Implementation of a Variational Auto-Encoder in TensorFlow

Tutorial_BayesianCompressionForDL. A tutorial on "Bayesian Compression for Deep Learning" published at NIPS (2017).

contextualLoss. The Contextual Loss

deeprl. Implementation of Reinforcement Learning Algorithms

Projects. List of projects

RL-ROBOT. Reinforcement Learning framework for Robotics

mlbop. Matlab code for S. Theodoridis' "Machine Learning: A Bayesian and Optimization Perspective" (2015).

Probabilistic-Programming-and-Bayesian-Methods-for-Hackers. aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)

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.