artificial intelligence

My own introspection

Package for generating and inverse-designing 2D lattice materials. Represents lattices as heterogeneous graphs and utilizes message passing, automatic differentiation and surrogate gradients for the inverse design.

Instance Hardness analysis in Machine Learning

Welcome to the "AI_Course_Template" - the basic repository template for students enrolled in the Artificial Intelligence course in the Department of Computer Science. This repository is designed to provide a standardized structure and set of guidelines for student projects, facilitating organized development, easy navigation, and consistent documentation.

Key Features:

Structured directory: Includes predefined folders for data (both raw and processed), source code, tests, and documentation, ensuring a clean and organized project workspace.

Comprehensive README Template: A guide to help students effectively document their project overview, goals, installation process, data descriptions, usage instructions, and contribution guidelines.

Resource Hub: Serves as a central location for essential resources, reference materials, and project-specific instructions.

Collaboration Ready: Configured to support collaborative projects, encouraging students to work together efficiently and share their progress.

Purpose:

This template is designed to streamline the project setup process, allowing students to focus more on the innovative aspects of AI and machine learning, rather than the initial setup and organization. By following this template, students will learn the importance of project organization, clear documentation, and consistent coding practices that are essential for any aspiring AI professional.

This project focuses on extracting and visualizing stock data using Python libraries such as yfinance for historical stock prices and web scraping techniques to gather company revenue data. It provides a comprehensive analysis by plotting both stock prices and revenues over time for companies like Tesla and GameStop.

Research, code, algorithms and all related to artificial intelligence

Un script avancé d’audit de sécurité et de pentesting alliant furtivité, détection adaptative, intelligence de vulnérabilités et analyses OS pour une évaluation réseau exhaustive. Conçu pour les professionnels, il allie automatisation et précision pour répondre aux environnements les plus complexes.

This project predicts house prices using machine learning models based on the King County House Sales dataset. It explores Simple Linear, Multiple Linear, Polynomial, and Ridge Regression models, comparing their performance in terms of accuracy. The best model identified is Polynomial Regression, achieving an R² score of 0.75.

Wally is a GitLab assistant powered by ChatGPT, an AI language model developed by OpenAI. With Wally, you can interact with your GitLab project using natural language commands and receive helpful suggestions and feedback from the AI.

The aim of N-Queens Problem is to place N queens on an N x N chessboard, in a way so that no queen is in conflict with the others.

This Python code employs the Depth-First Search (DFS) algorithm to find a path from "Arad" to "Bucharest" in the Romania map. It uses a priority queue, backtracking, and a graph representation. The DFS path is printed at the end. Skills demonstrated include graph traversal, priority queue usage, and DFS algorithm implementation.

The code uses backtracking to solve the Constraint Satisfaction Problem (CSP) of map coloring. It checks if assigning colors to regions follows the constraint that no adjacent regions have the same color. The skills involved include algorithm design for backtracking, recursion, and solving CSP problems. The implementation uses Python.

Implementation of UCS on Romania to find shortest distance from Arad to Bucharest based on best-first search algorithm. It uses a dictionary that stores current city mapped to (parent city, current best distance), and frontier is a priority queue to process nodes containing (node id, current city, priority value).

Neural Network for MicroPython

Your very own Assistant. Because you deserve it.

A Python library for Secure and Explainable Machine Learning

Documentation available @ https://secml.gitlab.io

Follow us on Twitter @ https://twitter.com/secml_py