Hi, I'm Pietrobon Andrea
- 🦾 I’m currently learning NLP and exploring Transformers
- 🤖 I'm interested in Machine Learning and Deep Learning
- 📚 Collaboration is the Key for building great things!
- 👨🏽💻 All of my projects are available at @Piero24
- 💬 Let's talk about Data Structures and Algorithms 😆
🧠 VanillaNet Cpp: (🔗Code | 🕹️Demo)
- A fully connected Neural Network in C++ with SGD from scratch, achieving 98% accuracy on the MNIST dataset.
- Developed with: C++, Cmake, Deep Learning
🏠 Airbnb Clone Website: (🔗Code | 🕹️Demo)
- Fully responsive Airbnb clone website featuring user authentication, property listings, and booking functionality
- Developed with: React, TypeScript, Tailwind CSS, MongoDB, Prisma, Next.js, Auth.js
🚚 Mathematical Optimization of the Traveling Salesman Problem: (🔗Code | 🕹️Results | 📄Paper)
- Application of different Algorithm to optimize a TSP problem achieving solutions within 3% of the optimum
- Developed with: CPLEX, C, Cmake
💬 Telegram Bot Amazon Offers: (🔗Code | 🕹️Demo)
- Find and select the best offers on Amazon and forward them to the Telegram channel
- Developed with: Python, SQLite3, pyTelegramBotAPI, Amazon Product Advertising API
🤖 Tiago Movement and Object Manipulation With ROS: (🔗Code | 🕹️Demo | 📄Paper)
- Operating tasks within a simulated space to recognize and maneuver around obstacles while capturing objects
- Developed with: C++, ROS, Moveit
🐙 HSNet+: Enhancing Polyp Segmentation with Region-wise Loss: (🔗Code | 🕹️Demo | 📄Paper)
- Combines the advantages of Transformer networks and CNN, along with regional loss for polyp segmentation
- Developed with: Python, PyTorch, OpenCV, Numpy
🍓 Food Recognition and Leftover Estimation: (🔗Code | 🕹️Demo | 📄Paper)
- Food recognition and leftover estimation with 95% more accurate performance than the previous version
- Developed with: C++, Cmake, OpenCV
- A telegram bot for each Italian region to see flight offers departing from airports in that region
- Developed with: Python, SQLite3, pyTelegramBotAPI, Airline companies API
📐 AutoLoss-Zero: Objective functions in training neural networks: (📄Paper)
- AutoML method, for the detection of colon polyps
- Developed with: Matlab, Deep Learning
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