Research for Parametric T-SNE in high to low dimensional data stream, published in 2021 by Kalebe Rodrigues Szlachta and Andre de Macedo Wlodkowski, oriented by Jean Paul Barddal, Computer Science graduation from Pontifical Catholic University of Parana (PUCPR)

GPU Accelerated t-SNE for CUDA with Python bindings

Machine-Learning-Algorithms-From-Scratch

My Dissertation evaluates methods against S&P 500 US stock data from Yahoo Finance, aiming to offer a concise financial market snapshot

Deep-based generation of Wing Interferential Patterns Images for the surveillance of blood-sucking insect population by Machine learning algorithms(Generative adversarial networks, Adversarial Autoencoders). Summer intership, research project

Undergraduate thesis for Bachelor in Computer Engineering

KLASIFIKASI DIABETIC RETINOPATHY MENGGUNAKAN ARSITEKTUR DEEP LEARNING MODEL CNN (CONVOLUTIONAL NEURAL NETWORK) RESNET50 224X224X3

This repository contains code base of my Master Thesis work named under "Pattern Detection in Tabular Data with shallow hierarchy: A Visual Analytics Case Study for Narrative Visualization"

A research paper recommender system that recommends similar research papers based on abstract text of the papers.

Recommendation Systems

MODE-TASK plugin for PyMOL

Pytorch implementation for t-SNE with cuda to accelerate

The objective of this problem is to explore the data, extract meaningful insights, and find different groups of vehicles in the data by using dimensionality reduction techniques like PCA and t-SNE.

Implementation of t-SNE and Barnes-Hut-SNE algorithm. Comparison of algorithm implementation with sklearn library implementation on sample databases.

Clustering algorithms to explore whether the patients can be placed into distinct groups. Then, you’ll create a visualisation to share your findings with your team and other key stakeholders.

Myopia Project: Using unsupervised machine learning to fit data into a model and using clustering algorithms to place data into groups. Then, create a visualisation to display a trend.

Performance of Kmean clustering algorithm on Myopia dataset

The first part of this project involves exploring the Auto-mpg dataset by applying dimensionality reduction techniques and visualizing the data in lower dimensions to extract insights. The second part involves segmenting a bank's customers to help the bank upgrade the service delivery model and ensure that customers' queries are resolved faster.