Dzaki Rafif Malik's starred repositories
Pytorch-UNet
PyTorch implementation of the U-Net for image semantic segmentation with high quality images
PyTorch-VAE
A Collection of Variational Autoencoders (VAE) in PyTorch.
Tools-to-Design-or-Visualize-Architecture-of-Neural-Network
Tools to Design or Visualize Architecture of Neural Network
UNetPlusPlus
[IEEE TMI] Official Implementation for UNet++
pygraphistry
PyGraphistry is a Python library to quickly load, shape, embed, and explore big graphs with the GPU-accelerated Graphistry visual graph analyzer
pytorch-segmentation
:art: Semantic segmentation models, datasets and losses implemented in PyTorch.
pyclustering
pyclustering is a Python, C++ data mining library.
medicaltorch
A medical imaging framework for Pytorch
Modern-Computer-Vision-with-PyTorch
Modern Computer Vision with PyTorch, published by Packt
DeepInfomaxPytorch
Learning deep representations by mutual information estimation and maximization
2020-CBMS-DoubleU-Net
Official implementation of DoubleU-Net for Semantic Image Segmentation in TensorFlow & Pytorch (Nominated for Best Paper Award (IEEE CBMS))
Default-of-Credit-Card-Clients-Dataset-Analisys
Analysis and classification using machine learning algorithms on the UCI Default of Credit Card Clients Dataset.
Clustering
ICAE code(An Image Clustering Auto-Encoder Based on Predefined Evenly-Distributed Class Centroids and MMD Distance)
tutorial-mininet
Tutorial project for getting started with Mininet
Credit-Card-Default-Prediction-End-to-End-Project
This is an end-to-end project that focuses on predicting credit card default using machine learning techniques. The project includes data validation,data preprocessing, model training, evaluation, and deployment.
Credit-Card-Default-Prediction
This project involved data preprocessing, model building, and deployment of a machine learning model to predict credit card default.
KPC_IM_128_64
This folder contains code for the paper titled "Information-maximization based clustering of histopathology images using deep learning"