Implementation of Machine Learning and Deep Learning techniques to find insights from the satellite data.

Gesture recognition library for Python

Official PyTorch implementation of PTS/PSRN: Fast and efficient symbolic expression discovery through parallelized tree search. Evaluates millions of expressions simultaneously on GPU with automated subtree reuse.

SoK: All You Need to Know About On-Device ML Model Extraction - The Gap Between Research and Practice

Smart Agriculture Using Sensors and Ai

Joint Deep Neural Network for Simultaneous Object and Depth Detection

A repository featurin BeautifulSoup for effective web scraping, enabling data extraction from diverse websites with practical examples and guides.

This repository contains an email spam detection system built using logistic regression, achieving an accuracy of 98%. The model was trained on a comprehensive dataset of labeled emails to effectively classify spam and non-spam messages.

Text Classification using Machine Learning

Compressive Strength of Concrete determines the quality of Concrete. analyze the Concrete Compressive Strength dataset and build a Machine Learning model to predict the quality.

AI-powered resume analyzer with job match recommendations.

Template alur kerja machine learning.

I'm a self-taught AI and Machine Learning developer, passionate about AI, Machine Learning, Computer Vision and learning new things. I have good experience working with the Python programming language and its libraries, and I am interested in computer vision and image processing using machine learning and deep learning algorithms.

MIST Machine Leaning in Cybersecurity Workshop Code Dump Repository

Here is my Bachelor's Degree Thesis, Music and Feelings: A Deep Learning Approach to Emotional Composition

This project aims to take an chest X-Ray image and detect if the patient has the COVID-19 infection. It uses a CNN to train on a large dataset of both normal and COVID lung images to learn how to process the difference in both images.

Implements and benchmarks optimal demonstration selection strategies for In-Context Learning (ICL) using LLMs. Covers IDS, RDES, Influence-based Selection, Se², and TopK+ConE across reasoning and classification tasks, analyzing the impact of example relevance, diversity, and ordering on model performance across multiple architectures.

Resolución del desafío de la clase cuatro, última clase de la serie Inmersión de Datos de AluraLatam

Developed a cutting-edge deep learning model to accurately analyze knee osteoarthritis from X-ray images. Leveraged convolutional neural networks (CNN) to enhance diagnostic precision, aiding in early detection and effective treatment planning. This project showcases my expertise in medical image analysis and advanced neural network architectures.