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About Myself:

Hi there šŸ‘‹

  • šŸŒ± Iā€™m currently learning advanced techniques in machine learning, including ensemble learning methods and deep learning architectures. Continuous learning is my mantra!
  • šŸ‘Æ Iā€™m looking to collaborate on exciting projects involving AI and machine learning. If you're interested in pushing the boundaries of technology, let's connect.
  • šŸ’¬ Ask me about my experience in deploying machine learning models, working with large datasets, handling out-of-distribution datasets, and collaborating in cross-functional teams. I'm always up for a chat about the fascinating world of data science.
  • šŸ“« How to reach me: Feel free to connect with me on my LinkedIn Profile. Let's discuss innovation and technology!
  • āš” Fun fact: When I'm not immersed in data and algorithms, I enjoy doing volunteer work and contributing to society.
Software skills šŸ’»:

Tensorflow Keras Pytorch XGBoost Sklearn Pandas Numpy PostgreSQL Docker

Mohamad Jaber's Projects

atten_deep_mil icon atten_deep_mil

This is an implementation of ICML 2018 "Attention-based Deep MIL"

auto-gpt icon auto-gpt

An experimental open-source attempt to make GPT-4 fully autonomous.

awesome_opensetrecognition_list icon awesome_opensetrecognition_list

A curated list of papers & resources linked to open set recognition, out-of-distribution, open set domain adaptation and open world recognition

dis icon dis

This is the repo for our new project Highly Accurate Dichotomous Image Segmentation

imbalanced-learn icon imbalanced-learn

A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning

keras-io icon keras-io

Keras documentation, hosted live at keras.io

models icon models

Models and examples built with TensorFlow

pydantic icon pydantic

Data validation using Python type hints

singleshotpose icon singleshotpose

This research project implements a real-time object detection and pose estimation method as described in the paper, Tekin et al. "Real-Time Seamless Single Shot 6D Object Pose Prediction", CVPR 2018. (https://arxiv.org/abs/1711.08848).

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