leilibrk/Smart-Home-Management-System

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Updated over 3 years ago

leilibrk/Smart-Home-Management-System

leilibrk

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This is my final project in the Logical Circuits course. In this project we designed a Smart Home Management System.

Reducing-noise-on-data

Reduced noise of noisy signals (with a proper desnoising rate) using Least Squares implemented in Python, Matplotlib and Numpy.

Github-FindUser

A mini project for web development course which fetches information such as Name, Location, Blog, Bio, and favorite language of a user from GitHub. Deployment: https://leilibrk.github.io/Github-FindUser/

Pacman-multiAgent

The phase 2 of my AI project, which is adversarial search in Pacman game for reaching the best utility and avoiding ghosts. Minimax with alpha-beta pruning and Expectimax is implemented.

Pacman-ReinforcementLearning

The phase 3 of my AI project, which is implementing model-based and model-free reinforcement learning algorithms, applied to the AIMA textbook's Gridworld, Pacman, and a simulated crawling robot.

Pacman-AI-Project

This is phase 1 of my AI project. By using different search algorithms ( DFS, BFS, UCS, A*), I tried to make Pacman do better in eating all the nodes and be alive.

Dining-philosophers

The dining philosophers problem is a classic synchronization problem which is used to evaluate situations where there is a need of allocating multiple resources to multiple processes.

EvolutionaryGame

Neuroevolutionary game.

XV6

XV6 project for operating system course. We had to add some system calls and add the thread features to the xv6 operating system.

Reducing-BMP-images

Reduced the size of .bmp images (up to 85%) using SVD implemented in Python, Matplotlib and Numpy.

Heart-disease-diagnosis-system

Fuzzy expert system for heart disease diagnosis

Compiler-Design

Custom Compiler Design project implementing lexing, parsing, and translation from a proprietary language to intermediate C code using PLY and Python

Image-Classification-Deep-Learning

Explore deep learning-powered image classification with PyTorch. Achieved 98% accuracy on Natural Images and 95% on Birds Species using AlexNet and EfficientNet-B1. Dive into the code and results!

Jupyter Notebook