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robertofranceschi/README.md

Hi, I'm Roberto 👋

🚀 I deal with Computer Vision and Robotics, working on cool multi-view 3D reconstruction projects.

💡 3+ years of hands-on experience in Computer Vision and Machine Learning, specializing in multi-view 3D reconstruction (NeRF, gsplat). Extensive experience in perception, training neural nets for Radar, Lidar, and Camera object detection.

🎓 M.Sc. in Data Science and Engineering @ Politecnico di Torino

🌐 Open to new opportunities in Machine Learning, Computer Vision, and - more broadly - AI and Software Engineering.

📫 How to reach me: LinkedIn

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  1. Incremental-learning-on-CIFAR100 Incremental-learning-on-CIFAR100 Public

    Forked from gabrieletiboni/Incremental-learning-on-CIFAR100

    Reproduction of popular methods for class-incremental learning in image recognition and proposal of a new variant.

    Jupyter Notebook 3

  2. DL-Radar-Detector-for-Complex-Automotive-Scenarios DL-Radar-Detector-for-Complex-Automotive-Scenarios Public

    DL-RTD: End-to-end CNN Radar Target Detection in Complex Automotive Scenarios

    4 1

  3. Domain-Adaptation-on-PACS-dataset Domain-Adaptation-on-PACS-dataset Public

    Implementation of Domain adaptation on PACS dataset using a modified version of Alexnet.

    Python 21 2

  4. Default-Credit-Card-Prediction Default-Credit-Card-Prediction Public

    Analysis and classification using machine learning algorithms on the UCI Default of Credit Card Clients Dataset.

    Jupyter Notebook 19 10

  5. Lung-Cancer-Subtyping-using-GANs Lung-Cancer-Subtyping-using-GANs Public

    Implementation Subtype-GAN in PyTorch working on multi-omics data. Unsupervised Image segmentation applied to SVS images.

    Jupyter Notebook

  6. Sentiment-Analysis-on-Tripadvisor-reviews Sentiment-Analysis-on-Tripadvisor-reviews Public

    Sentiment analysis, analyzing user’s textual reviews, to perform a binary classification task (positive or negative mood). Designed a machine learning pipeline able to achieve an F1 score of 96.7% …

    Python 5