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10 followers · 5 following
  • Helsinki, Finland

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Achievement: Pull Sharkx3Achievement: Pair ExtraordinaireAchievement: YOLO
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mihaelsouza/README.md

Greetings and Salutations, 👋

I'm Mihael, and I'm a researcher turned into Software Developer.

Over time I realized that most of the accomplishments I was proud of from my time in academia were coding/software related, so I thought it was about time to take the plunge into this vast universe of software development.

I am passionate about data, probably because I've dealt with a lot of it during my research 😅, and fascinated about machine learning and AI. I started learning programming with MATLAB and R, but really fell in love with it when I started using Python.

I'm currently invested in getting better with full-stack development 🌱 using technologies under the TypeScript/JavaScript umbrella. I want to keep building interesting models and data-driven applications using Python and Tensorflow/Keras on the backend, and make them available to users on the browser and mobile. What good is a nice model/AI if no one can enjoy its benefits, right? 💥

Want to collaborate? 👯 Hit me up and let's build cool things together!

Nice to meet you :bowtie:

Pinned

  1. ivangelus/master-of-yoga ivangelus/master-of-yoga Public

    Improve your form and guarantee greater benefits from your Yoga sessions by leveraging the power of Machine Learning. Built with TypeScript, React, and Python.

    TypeScript 1 1

  2. TwtSavy TwtSavy Public

    Analyse sentiment of tweets or Twitter accounts from the palm of your hand, leveraging the power of a custom built AI! Built in ReactNative and NestJS, serving the AI using Django as a microservice.

    TypeScript 1

  3. paleo_dashboard paleo_dashboard Public

    Dashboard showcasing locations and available properties of sediment cores in the arctic circle. Built in HTML/CSS and JS, with the D3.js framework.

    JavaScript

  4. Hamburg_neighborhoods_recommender Hamburg_neighborhoods_recommender Public

    Implementation of a Recommender System to suggest good neighborhoods to live in based on a set of user preferences. Implemented using Python and Jupyter Notebooks.

    Jupyter Notebook