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Hello, I'm Charaf πŸ‘‹

I have been coding since age 15, interested in quantitative finance, computer vision, and agent-based models in economics and finance.

  • πŸ”­ I’m currently working on agent-based models and employed as a Data Scientist at BNP PARIBAS.
  • πŸŽ“ Studying at Paris 1 PanthΓ©on-Sorbonne.
  • 🌱 I’m deeply passionate about many things including biology, physics, mathematics, and philosophy.
  • πŸ‘― I’m looking to collaborate with other programmers/data scientists.
  • πŸ₯… 2024 Goals: Contribute to more Quant finance projects, do more courses on YouTube.
  • ⚑ Fun fact: I love to play chess and enjoy watching/doing stand-up comedy.

Connect with me:

LinkedIn Youtube Portfolio

Languages and Tools:

Python JavaScript C++ Java

My GitHub Statistics:

Charaf's GitHub stats

Top Langs

Charaf ZGUIOUAR's Projects

angular-gettingstarted icon angular-gettingstarted

Sample Angular application used in the "Angular: Getting Started" course: http://bit.ly/Angular-GettingStarted

awesome-quant icon awesome-quant

A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance)

bayesianneuralnetwork-tutorial-metarepos icon bayesianneuralnetwork-tutorial-metarepos

A meta repository pointing to the other repositories where the implementation of the supplementary examples for our tutorial "Hands-on Bayesian Neural Networks - A Tutorial for Deep Learning Users"

deep-learning-course icon deep-learning-course

Washington University (in St. Louis) Course T81-558: Applications of Deep Neural Networks

deep-learning-ultra icon deep-learning-ultra

Open source Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in PyTorch, OpenCV (compiled for GPU), TensorFlow 2 for GPU, PyG and NVIDIA RAPIDS

deeplearningexamples icon deeplearningexamples

State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.

dermanpapers icon dermanpapers

Notebooks that replicate original quantitative finance papers from Emanuel Derman

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