andriash001

eT2QFNN

This branch presents an evolving model based on a novel evolving intelligent system, namely evolving Type-2 Quantum Fuzzy Neural Network (eT2QFNN), which features an interval type-2 quantum membership function with uncertain jump positions.

DEVDAN

An automated construction of a denoising autoeconder is presented here. It features an open structure both in the generative phase and in the discriminative phase where input features can be automatically added and discarded on the fly and free of the problem- specific threshold.

ADL

This code refers to all experiments in our paper "Autonomous Deep Learning: Continual Learning Approach for Dynamic Environments"

AutonomousDCN

This code executes Autonomous Deep Clustering Network (ADCN)

ADLPlus-DEVDANPlus

Modified version of ADL and DEVDAN: Implement multiple node growing scenario and adaptive synapses pruning

Deep-Learning-in-Production

In this repository, I will share some useful notes and references about deploying deep learning-based models in production.

18S1-CI6227-DATA-MINING

This code implements catboost algorithm.

ADL-1

Autonomous Deep Learning: Continual Learning Approach for Dynamic Environments. Proceedings of the 2019 SIAM International Conference on Data Mining.

adl_python

This is the implementation of Autonomous Deep Learning on python

DEVDAN-1

DEVDAN: Deep Evolving Denoising Autoencoder. Neurocomputing 2019.

NADINE

Automatic Construction of Multi-layer Perceptron Network from Streaming Examples. The 28th ACM International Conference on Information and Knowledge Management (CIKM).

ParsNet

Weakly Supervised Deep Learning Approach in Streaming Environments. Proceedings of The 2019 IEEE International Conference on Big Data (IEEE Big Data 2019).

bayesian-neural-network-mnist

Bayesian neural network using Pyro and PyTorch on MNIST dataset

bayesian-neural-network-pytorch

Bayesian Neural Network For Pytorch

blitz-bayesian-deep-learning

A simple and extensible library to create Bayesian Neural Network layers on PyTorch.

CE7454_2018

Deep learning course CE7454, 2018

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.

FreeCodeCamp-Pandas-Real-Life-Example

Information-Retrieval-and-Analysis

CI6226 Assignment

just-pandas-things

An ongoing list of pandas quirks

LLDEN

Lifelong Learning with Dynamically Expandable Networks

PyTorch-BayesianCNN

Bayesian Convolutional Neural Network with Variational Inference based on Bayes by Backprop in PyTorch.

pytorch-beginner

pytorch tutorial for beginners

pytorch-yolo-v3

A PyTorch implementation of the YOLO v3 object detection algorithm

yolov3workflow

This workflow aims to make it easy to train and execute Yolo v3 object detection on Windows.