gjkaur

gjkaur's Stars

• SanDiegoMachineLearning/bookclub

  Notes and links from the book club meetings

  Language:Jupyter Notebook502104

• mrdbourke/pytorch-deep-learning

  Materials for the Learn PyTorch for Deep Learning: Zero to Mastery course.

  Language:Jupyter Notebook10.9k3.2k

• gjkaur/PhD_Thesis_and_Research_Publications

  1

• gjkaur/Data_Science

  100_days_of_learning

  Language:Jupyter Notebook1

• ankitaj85/kubeflow-sdk-demo

  Language:Jupyter Notebook420

• v-sonawane/mlops-playbook

  The MLOps Playbook is designed to provide a centralized hub of curated resources for studying and mastering the principles, tools, and best practices of MLOps.

  121

• data-science-on-aws/data-science-on-aws

  AI and Machine Learning with Kubeflow, Amazon EKS, and SageMaker

  Language:Jupyter Notebook3.3k1.1k

• josephmisiti/awesome-machine-learning

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

  Language:Python65.8k14.6k

• JZK00/Radiomics-Features-Extractor

  Hand-crafted radiomics and deep learning-based radiomcis features extraction.

  Language:Python7718

• pramod-zillella/Brain-Tumor-Segmentation

  Fully automatic brain tumor segmentation using the Modified 3DUNet architecture for Brats 2020 Challenge.

  Language:Jupyter Notebook161

• Artur112/Brain_Tumour_Segmentation

  Code for training a 3DUnet for Brain tumour segmentation from Brats 2019 dataset; for feature extraction from the segmented volumes and for survival prediction. Run train.py for training, segment.py for segmenting test scans and evaluate.py for evaluating the performance of those segmentations. Basic code also written to perform survival prediction with a random forest classifiier.

  Language:Python228

• NabJa/deep-brats

  Data Analysis and deep learning on BraTS data

  Language:Python11

• koriavinash1/BioExp

  Explainability of Deep Learning Models

  Language:Python285

• razeineldin/DeepSeg

  DeepSeg: Deep Neural Network Framework for Automatic Brain Tumor Segmentation using Magnetic Resonance FLAIR Images

  Language:Python2613

• gsel9/biorad

  Extract quantitative features from medical images with the pyradiomics package, and perform machine learning experiments combining the SMAC framework and scikit-learn module.

  Language:Jupyter Notebook72

• kwahid/Radiomic-Prediction-of-Tumor-Grade-and-Overall-Survival-from-the-BraTS-Glioma-Dataset

  Folder corresponding to 2017 summer project at MD Anderson Cancer Center.

  Language:Jupyter Notebook1213

• PyRetri/PyRetri

  Open source deep learning based unsupervised image retrieval toolbox built on PyTorch🔥

  Language:Python1.2k178

• PacktPublishing/Hands-On-Image-Processing-with-Python

  Language:Jupyter Notebook281192

• Dhanya-Abhirami/Brain-Tumour-Segmentation

  Use of Image Processing to detect brain tumour in MRI Scan

  Language:Jupyter Notebook217