Name: Ibraheem Al-Dhamari
Type: User
Company: RGSE, Koblenz
Bio: Ibraheem Al-Dhamari. Computer Science Researcher.
Interest: image processing, medical image analysis, deep learning, and cellular automata.
Location: Germany
Blog: https://idhamari.com
Ibraheem Al-Dhamari's Projects
ARXaaS is a "Anonymization as a Service" project built ontop of the ARX library
This is my personal template collection. Here you'll find templates, and configurations for various tools, and technologies.
Code for Image Synthesis with a Convolutional Capsule Generative Adversarial Network
Free cool CV templates
Deep Learning Specialization by Andrew Ng, deeplearning.ai.
Multi user Jupyterhub with C++, Java, Python, Tensorflow, Julia, SQL, NodeJS, Bash and more!
Docker with Keycloak and Traefik Workshop
Large language models (LLMs) made easy, EasyLM is a one stop solution for pre-training, finetuning, evaluating and serving LLMs in JAX/Flax.
A robot powered training repository :robot:
Practical tutorials for data privacy
Settings for different servers
Interactive field-aligned mesh generator
Multi-user server for Jupyter notebooks
ARCHIVED Containers for the no longer supported WildFly distribution of Keycloak
Large Deformation Diffeomorphic Image Registration with Laplacian Pyramid Networks
A resource for learning about ML, DL, PyTorch and TensorFlow. Feedback always appreciated :)
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.
The template repository for the Medical Image Registration course on Learning Lab.
Probabilistic Dense Displacement Network (3D discrete deep learning registration)
Mirror of Apache POI
Learn by experimenting on state-of-the-art machine learning models and algorithms with Jupyter Notebooks.
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.
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.