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Domenico

I'm 27 years old Student of the Master’s Degree course in Artificial Intelligence

  • 🧠 I’m currently learning Deep Learning models
  • 😄 Pronouns: he/him
  • 💻 Languages and Tools: Java, C, C++, Python3, MATLAB, HTML, CSS, Javascript, JQuery, OCaml, PHP, PHPUnit, MySQL, MongoDb, Postgress, React, Symfony, Docker, Tensorflow, Keras
  • 🧐 Fun fact : Food and Music🎵 are the reasons to live.

Top Langs

Domenico's Projects

chatterbox icon chatterbox

development in C of a concurrent server that implements a chat, users can exchange text messages / files by connecting to the server with a client program

cnn_adversarial_attack icon cnn_adversarial_attack

Implementation of personalized convolutional network and show performance results on CIFAR-10. After network is trained, try an adversarial attack to it with the simple Fast Gradient Sign method, generating one (or more) adversarial examples starting from one (or more) CIFAR-10 test images. At the end display the original image, the adversarial noise and the final adversarial example.

cpp_with_docker icon cpp_with_docker

Tools for running Cpp applications on Docker with docker-compose and local volume

exams icon exams

Collection of oral questions and exam material

greenapp icon greenapp

Android app for remote control of an IoT system

hmm_gaussian_emissions icon hmm_gaussian_emissions

Hidden Markov Model with Gaussian emissions of the dataset which measure the energy consumption of appliances and lights, across a period of 4.5 months. Once trained the HMMs, perform Viterbi on a reasonably sized subsequence (e.g. 1 month of data), plot the timeseries data highlighting (e.g. with different colours) the hidden state assigned to each timepoint by the Viterbi algorithm. Then,sampling a sequence of at least 100 points from the trained HMMs and show it on a plot discussing similarities and differences w.r.t. the ground truth data.

image-recognition icon image-recognition

This repository contains the source code of an image recognition system using deep learning techniques, with a focus on leveraging the Keras framework in Python. The primary objective of this project is to create a robust and accurate model capable of identifying objects or patterns

java_data_container icon java_data_container

the project consists in designing, implementing and documenting the SecureDataContainer <E> collection. SecureDataContainer <E> is a container of type E objects. Intuitively the collection is constituted as a species Data storage for data specification and sharing (represented in the simulation from type E objects). The collection guarantees a Data Security mechanism providing its own user identity management mechanism. Furthermore, it provides a mechanism for access control that allows the data owner to perform a selective restriction access to your data included in the collection. Some users can be set by the owner to access the data, while others cannot access it without authorization.

kmeans_with_sift_descriptors icon kmeans_with_sift_descriptors

Select an image from 4 different themes, for these images, extract the SIFT descriptors using the visual feature detector embedded in SIFT to identify the points of interest. Aggregate all the identified descriptors in a dataset and run k-means to partition the descriptors in clusters. Then analyze the obtained clusters by confronting the descriptors assigned to each cluster with the thematic classes of the images from which they were extracted (confusion matrix ).

neural_network_from_scratch icon neural_network_from_scratch

implementation of an Artificial Intelligence Neural Network from scratch using Python programming language with the use of k-fold cross validation, grid search and ensamble.

openmp_code_examples icon openmp_code_examples

Some code with the usage of OpenMP multi-platform shared-memory parallel programming in C/C++

rui icon rui

web platform for IoT - AI - Industry 4.0 which is based on 4 docker containers

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