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versi379/README.md

Giovanni Versiglioni

Relevant Projects

Software Engineering: Localization Manager - Developed a real-time localization tool with language switching and RTL support.

HPC: Optimized Matrix Multiplication - Achieved 5x speedup in matrix multiplication with cuBLAS and optimized memory usage.

Computer Vision: Two-View and Three-View Pose Estimation - Compared 2-view and 3-view pose estimation algorithms for 3D reconstruction.

Deep Learning: Dog Breed Classification - Built a CNN model with 83% accuracy on Stanford Dogs Dataset.

Machine Learning: Twitter Sentiment Analysis - Achieved 90% accuracy for sentiment analysis of tweets using BERT and roBERTa.

Software Engineering: Multiplayer Board Game - Developed a real-time multiplayer game with socket/RMI for up to 10 players.

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  1. Localization-Manager-QA Localization-Manager-QA Public

    A tool for real-time localization testing with dynamic language switching, RTL layout support, and automated detection of text overflow and alignment issues, generating detailed QA reports.

    Python

  2. Optimized-Matrix-Multiplication Optimized-Matrix-Multiplication Public

    This project utilizes CUDA and cuBLAS to optimize matrix multiplication, achieving up to a 5x speedup on large matrices by leveraging GPU acceleration. It also improves memory efficiency and reduce…

    C++

  3. Two-View-Three-View-Pose-Estimation Two-View-Three-View-Pose-Estimation Public

    Comparative Analysis of Two-View and Three-View Pose Estimation Algorithms for Image-Based 3D Reconstruction: Fundamental Matrix vs Trifocal Tensor

    TeX

  4. AR-Object-Detection AR-Object-Detection Public

    A project that detects objects and displays 3D labels in augmented reality, serving as a basic template for using Core ML with ARKit.

    Swift

  5. Dog-Breed-Classification Dog-Breed-Classification Public

    Stanford dogs dataset breed classification with Xception (CNN)

    Jupyter Notebook

  6. Twitter-Sentiment-Analysis Twitter-Sentiment-Analysis Public

    Sentiment analysis using BERT and roBERTa to predict the emotion of tweets about coronavirus

    Jupyter Notebook