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arxaas

ARXaaS is a "Anonymization as a Service" project built ontop of the ARX library

boilerplates

This is my personal template collection. Here you'll find templates, and configurations for various tools, and technologies.

capspix2pix

Code for Image Synthesis with a Convolutional Capsule Generative Adversarial Network

cv-templates

Free cool CV templates

deep-learning-coursera

Deep Learning Specialization by Andrew Ng, deeplearning.ai.

differential_privacy_tutorials

docker-jupyterhub

Multi user Jupyterhub with C++, Java, Python, Tensorflow, Julia, SQL, NodeJS, Bash and more!

docker-keycloak-traefik-workshop

Docker with Keycloak and Traefik Workshop

easylm

Large language models (LLMs) made easy, EasyLM is a one stop solution for pre-training, finetuning, evaluating and serving LLMs in JAX/Flax.

github-slideshow

A robot powered training repository :robot:

ia-data-privacy-tutorials

Practical tutorials for data privacy

ia-image-processing

ia_servers

Settings for different servers

instant-meshes

Interactive field-aligned mesh generator

java-functional-programming

jupyterhub

Multi-user server for Jupyter notebooks

keycloak-containers

ARCHIVED Containers for the no longer supported WildFly distribution of Keycloak

lapirn

Large Deformation Diffeomorphic Image Registration with Laplacian Pyramid Networks

learning_without_metric

machine-learning-collection

A resource for learning about ML, DL, PyTorch and TensorFlow. Feedback always appreciated :)

medical-image-registration-course

Learn medical image registration in a simple and practical way using python notebook. In this course, I try to cover the math and the theory using simple examples.

medical-image-registration-template

The template repository for the Medical Image Registration course on Learning Lab.

medical_informatics

medicaldataaugmentationtool-heatmapuncertainty

pdd2.5

pdd_net

Probabilistic Dense Displacement Network (3D discrete deep learning registration)

poi

Mirror of Apache POI

practical-ml

Learn by experimenting on state-of-the-art machine learning models and algorithms with Jupyter Notebooks.

privacy-in-statistics-and-machine-learning

Outlines: This is a practical tutorial based on Adam Smith Course. I will try to explain the math using examples and python code. This will help beginners to get better understanding! The original course includes video lectures and slides so I suggest you start there and then practice here. Each topic will have its own colab notebook. All notebooks will be available publicly in github. You can use the [github discussion] section to ask questions and hopfully getting answers. Notes: I will use photos and resources from these lectures and other resources, all source links will be available.

profs-list

List of all professors in specific field, Useful when searching for a supervisor, a collaboration . If someone is missing, please add them using pull request. This probably will be useful until LLMs such as chatGPT are improved.