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Hi there πŸ‘‹, I am Anas Hasni!

I am an AWS Community Builder and a Deep Learning practitioner.

I am interested in Computer Vision and NLP, I hold a Master's Degree in Intelligent and Communicating Systems from National School of Applied Sciences, Morocco. I also have experience working as Machine Learning Engineer/ Research Assistant at INSEA, Morocco.

I mainly work with Python, Keras, Tensorflow, and Pytorch.

  • πŸ”­ I’m currently working on several Deep Learning Projects Zindi || Kaggle || Hugging Face
  • 🌱 I’m currently learning MLOps.
  • πŸ‘― I’m looking to collaborate on Deep Learning Projects
  • πŸ“« How to reach me: DM me on LinkedIn, Twitter
  • πŸ˜„ Pronouns: He/Him

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Anas's Projects

90daysofdevops icon 90daysofdevops

This repository is my documenting repository for learning the world of DevOps. I started this journey on the 1st January 2022 and I plan to run to March 31st for a complete 90-day romp on spending an hour a day including weekends to get a foundational knowledge across a lot of different areas that make up DevOps.

amazon-sagemaker-examples icon amazon-sagemaker-examples

Example πŸ““ Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.

animegan icon animegan

A Tensorflow implementation of AnimeGAN for fast photo animation ! This is the Open source of the paper γ€ŒAnimeGAN: a novel lightweight GAN for photo animation」, which uses the GAN framwork to transform real-world photos into anime images.

applied-ml icon applied-ml

πŸ“š Papers & articles of companies sharing their work on applied data science & machine learning.

best-of-ml-python icon best-of-ml-python

πŸ† A ranked list of awesome machine learning Python libraries. Updated weekly.

d2l-pytorch icon d2l-pytorch

This project reproduces the book Dive Into Deep Learning (www.d2l.ai), adapting the code from MXNet into PyTorch.

deep-learning-drizzle icon deep-learning-drizzle

Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

deep-learning-in-production icon deep-learning-in-production

Build, train, deploy, scale and maintain deep learning models. Understand ML infrastructure and MLOps using hands-on examples.

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