Habib Mrad's Projects
prostatecancer.ai is an AI-based, zero-footprint medical image viewer that can identify clinically significant prostate cancer.
Standardized data set for machine learning of protein structure
Single page cheat-sheet about Python string formatting
Personalized Genomics and Proteomics. Main diet: Ensembl, side dishes: SNPs
Warning: This project does not have any current developer. See bellow.
Pandas in Medicine
Python SDK for high performance on-line Brain Computer Interface development.
The "Python Machine Learning" book code repository and info resource
Open-source python package for the extraction of Radiomics features from 2D and 3D images and binary masks. Support: https://discourse.slicer.org/c/community/radiomics
Python implementation of Andrew Ng's ML course projects
Python classifier for the PhysioNet/CinC Challenge 2021
Tutorial and introduction into programming with Python for the humanities and social sciences
Code repository for Python Deep Learning, published by Packt
Python Deep Learning Cookbook, published by Packt
Python Deep Learning Projects, published by Packt
Python for Genomic Data Science 2015 Coursera from Johns Hopkins University
Python for Genomic Data Science from Universidad Johns Hopkins.
Python Natural Language Processing Cookbook, published by Packt
Python Reinforcement Learning Projects, published by Packt
Data Science, Visualisations, and Machine Learning Cookbook
Python Data Science Handbook: full text in Jupyter Notebooks
Verify Python code submissions and auto-generate meaningful feedback messages.
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Practice on cifar100(ResNet, DenseNet, VGG, GoogleNet, InceptionV3, InceptionV4, Inception-ResNetv2, Xception, Resnet In Resnet, ResNext,ShuffleNet, ShuffleNetv2, MobileNet, MobileNetv2, SqueezeNet, NasNet, Residual Attention Network, SENet, WideResNet)
Image-to-Image Translation in PyTorch
Deep Learning (with PyTorch)
Materials for the Learn PyTorch for Deep Learning: Zero to Mastery course.
core code for High-Capacity Convolutional Video Steganography with Temporal Residual Modeling