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Achievements

Achievement: StarstruckAchievement: Pair ExtraordinaireAchievement: Pull SharkAchievement: YOLO

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federicobrancasi/README.md

👋 Hey, I'm Federico - Let's Code the Future Together!

Greetings from my coding corner! I'm Federico, an enthusiastic explorer in the expansive realm of computer science. Currently, I'm at ETH Zurich working on my Master's thesis in AI model quantization, while completing my Double M.S. in Computer Science at the University of Trento and Eötvös Loránd University in Budapest. I'm on the lookout for like-minded visionaries eager to shape the future alongside me.

🌐 Let's Connect and Collaborate

🛠️ Peek into My Coding World

Here are some carefully selected projects I have on GitHub:

  1. Deep Learning Course Book

    • A comprehensive LaTeX-based book for the Deep Learning course at the University of Trento, designed to assist future students.
    • Custom illustrations created using the TikZ library, covering key concepts and techniques in deep learning.
    • Tech Stack: LaTeX, TikZ
    • GitHub: Explore Project

  2. DeepQuant - Neural Network Quantization Framework

    • A powerful library for converting Brevitas-quantized neural networks into true integer-only computation graphs, enabling efficient deployment on edge devices.
    • Developed at the Integrated Systems Laboratory (IIS), ETH Zurich
    • Tech Stack: Python, PyTorch, ONNX
    • GitHub: Explore Project

  3. Enhanced File Explorer for Chrome

    • Improves Chrome's file explorer when browsing local files, compatible with all the Chromium-based browsers.
    • Over 900 daily users and 5000+ installations
    • Tech Stack: JavaScript, Chrome Extension API
    • GitHub: Explore Project

  4. Test-Time Adaptation for Image Classification

    • Explores innovative approaches to enhance the robustness of pre-trained neural networks for image classification when faced with out-of-distribution test data.
    • Implements Test-Time Adaptation techniques, leverages Vision-Language Models, and explores prompt engineering and learning.
    • Tech Stack: Python, PyTorch, AWS
    • GitHub: Explore Project

  5. Mathematics for Machine Learning

    • Detailed notes and exercises for the "Mathematics for Machine Learning" specialization at Imperial College London
    • Cheatsheets on Linear Algebra, Multivariate Calculus, and Principal Component Analysis
    • Tech Stack: Python, Jupyter Notebooks
    • GitHub: Explore Project

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  1. Deep-Learning Deep-Learning Public

    Comprehensive LaTeX-based deep learning guide with custom TikZ illustrations

    TeX 57 15

  2. pulp-platform/DeepQuant pulp-platform/DeepQuant Public

    A Python library for true quantization of neural networks

    Python 7 1

  3. Mathematics Mathematics Public

    Detailed notes and exercises for the "Mathematics for Machine Learning" specialization of Imperial College London.

    Jupyter Notebook 2

  4. Enhanced-File-Explorer-for-Chrome Enhanced-File-Explorer-for-Chrome Public

    Improves chrome's file explorer when browsing local files.

    CSS 2

  5. Test-Time-Adaptation-for-Image-Classification Test-Time-Adaptation-for-Image-Classification Public

    Enhances neural network robustness for image classification

    Jupyter Notebook 1 1

  6. Kaggle-Courses Kaggle-Courses Public

    My Exercises from Kaggle's Data Science and Machine Learning Courses.

    Jupyter Notebook 1 1