andjelatodorovic Goto Github PK

2021summerschool

andjelatodorovic

attentionunet_lungsegmentation

audio_analysis

autoencoder

Simple autoencoder to compress the MNIST dataset. With autoencoders, we pass input data through an encoder that makes a compressed representation of the input. Then, this representation is passed through a decoder to reconstruct the input data. Generally the encoder and decoder will be built with neural networks, then trained on example data. In another file a convolutional layer is added for better results.

awesome-gan-for-medical-imaging

Awesome GAN for Medical Imaging

awesome-hypergraph-network

A curated list of Hypergraph Learning, Hypergraph Theory, Hypergraph Dataset and Hypergraph Tool.

awesome-persian-nlp-ir

Curated List of Persian Natural Language Processing and Information Retrieval Tools and Resources

batchnormalization

Batch normalization was introduced in Sergey Ioffe's and Christian Szegedy's 2015 paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. The idea is that, instead of just normalizing the inputs to the network, we normalize the inputs to layers within the network. It's called "batch" normalization because during training, we normalize each layer's inputs by using the mean and variance of the values in the current mini-batch.

bda

bottleneck

Code for the paper: "On the Bottleneck of Graph Neural Networks and Its Practical Implications"

cancertissuesegmentation

Image processing and segmentation using the Gaussian mixture model

chessdatabase

University project of a Chess Club Database, scripted in SQL and PHP, given a certain model to implement

codeai

Our solution for predicting soil functional properties at unsampled locations using infrared spectroscopy, georeferencing of soil samples, and earth remote sensing data. Won a first place and a chance to visitWe Are Developers conference in Berlin.

conf-mc

Conformalized matrix completion - reproduction and personal efforts

connect4ai

The Ai that learns to play Connect4 by using Markov Chains Monte Carlo Method.

coordinate_based_mlps

Experiment of coordinate based MLPs Network with Pytorch-Lightning

cudademo

Basic coding examples in C/C++ using CUDA on NVIDIA Jetson Nano.

cudnnsamples

Samples of NVIDIAS' CuDNN. The NVIDIA CUDA® Deep Neural Network library (cuDNN) is a GPU-accelerated library of primitives for deep neural networks. cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers.

data-science--cheat-sheet

Cheat Sheets

datastructures

Demos of the implementation of the basic, both linear and non-linear, data structures in C/C++.

datathonibm

Credit risk prediction model for large logistics & distribution company. The idea behind the solution was to use the Altman Z credit score in order to turn the unsupervised into semi-supervised problem, and then apply self-training methods.

dbscan

Density-based spatial clustering of applications with noise (DBSCAN) is a well-known data clustering algorithm that is commonly used in data mining and machine learning.

dcgan

DCGAN (Deep convolutional generative adversarial networks) is one of the popular and successful network design for GAN. It mainly composes of convolution layers without max pooling or fully connected layers. It uses convolutional stride and transposed convolution for the downsampling and the upsampling. The figure below is the network design for the generator.

deep_mlo

deeplearning-models

A collection of various deep learning architectures, models, and tips

deeplearningdemo

These are some of the IPython Notebooks that I've been working on, and follow-up pure Python3 code will be released in separate repositories.

deeplearningpurepython3

Pure Python3 codes for the Deep Learning Coursera repositories.

deeplearningspecialization

My solutions to the Deep Learning Specialization on Coursera.

designpatterns

A solution to some of the previous University exams and homework assignments from the course on the Object Oriented Programming. Some lecture notes are to be uploaded