gplichoski

brasil.io

Backend da API do https://Brasil.IO/

codenation-challenge-4-pca-and-rfe

This repository is for codenation challenge 4 about PCA and RFE.

codenation-challenge2-prepocessing-with-python

This repository refers to codenation data science course first code challenge.

codenation-challenge3-probability-distribution

This repository is for codenation data science course probability distribution challenge.

curso-javascript-ninja

Curso Javascript Ninja

face-recognition-framework-bio-inspired-algorithms

This repository contains the project from my master's thesis. It is well known that Face Recognition (FR) systems are still facing great challenges when variations of illumination, pose, expression and occlusion are present. Also, in many situations, it is only possible to obtain One Sample Per Person (OSPP) for training, representing a challenging real-world condition. In this work, we promote an FR solution that approaches the illumination variation challenge, along with the OSPP problem. The proposed FR framework is defined by an optimizer and a pool of preprocessing and feature extraction techniques. The approach makes available to the optimizer a pool of techniques, in which the optimizer seeks for the best set of strategies, also tuning their parameters. In this work, the FR framework uses the well-known Differential Evolution algorithm as the optimizer, called FR-DE. Two experimental methodologies are employed to assess the performance of the proposed FR-DE framework. The first methodology employs a standard dataset separation and uses the Yale Extended B dataset presenting severe illumination variation conditions. The second experimental methodology considers the OSPP problem along with illumination and poses variations. The CMU-PIE and FERET datasets are employed. The FR-DE is compared with some state-of-art algorithms and analysis suggest that the proposed framework is competitive and suitable for face recognition.

introduction-to-ML-with-python

Examples of the book Introduction to Machine Learning with Python

kaggle-titanic

keras-explore

This repository was created to explore the python library Keras.

optimization-algorithms

This repository contains some optimization algorithms in Python, namely Particle Swarm Optimization Algorithm, Differential Evolution, Self Adaptive Differential Evolution and Differential Evolution with Composite Trial Vector Generation Strategies and Control Parameters.

gplichoski/optimization-algorithms

This repository contains some optimization algorithms in Python, namely Particle Swarm Optimization Algorithm, Differential Evolution, Self Adaptive Differential Evolution and Differential Evolution with Composite Trial Vector Generation Strategies and Control Parameters.

gplichoski/keras-explore

This repository was created to explore the python library Keras.

gplichoski/brasil.io

Backend da API do https://Brasil.IO/

gplichoski/codenation-challenge-4-pca-and-rfe

This repository is for codenation challenge 4 about PCA and RFE.

gplichoski/codenation-challenge2-prepocessing-with-python

This repository refers to codenation data science course first code challenge.

gplichoski/codenation-challenge3-probability-distribution

This repository is for codenation data science course probability distribution challenge.

gplichoski/curso-javascript-ninja

Curso Javascript Ninja

gplichoski/face-recognition-framework-bio-inspired-algorithms

This repository contains the project from my master's thesis. It is well known that Face Recognition (FR) systems are still facing great challenges when variations of illumination, pose, expression and occlusion are present. Also, in many situations, it is only possible to obtain One Sample Per Person (OSPP) for training, representing a challenging real-world condition. In this work, we promote an FR solution that approaches the illumination variation challenge, along with the OSPP problem. The proposed FR framework is defined by an optimizer and a pool of preprocessing and feature extraction techniques. The approach makes available to the optimizer a pool of techniques, in which the optimizer seeks for the best set of strategies, also tuning their parameters. In this work, the FR framework uses the well-known Differential Evolution algorithm as the optimizer, called FR-DE. Two experimental methodologies are employed to assess the performance of the proposed FR-DE framework. The first methodology employs a standard dataset separation and uses the Yale Extended B dataset presenting severe illumination variation conditions. The second experimental methodology considers the OSPP problem along with illumination and poses variations. The CMU-PIE and FERET datasets are employed. The FR-DE is compared with some state-of-art algorithms and analysis suggest that the proposed framework is competitive and suitable for face recognition.

gplichoski/introduction-to-ML-with-python

Examples of the book Introduction to Machine Learning with Python

gplichoski/kaggle-titanic

gplichoski/knn-classifier

This repository contains a project using k-NN (k-Nearest Neighbors) classifier. As example, five datasets are included in the project i.e. the famous iris, e-coli proteins, yeasts proteins, wine chemistry and weat seeds.

gplichoski/minicurso-POO-java

Este repositório contém classe, aplicativos e a apresentação do minicurso de POO com Java apresentado na SEI da UDESC Joinville.

gplichoski/ml4leads

This repository is for codenation project of suggesting leads given a portfolio

gplichoski/pandas-explore

In this repository I explore the python library pandas.

gplichoski/project-based-learning

Curated list of project-based tutorials

gplichoski/ruby-wsdl-soap-integration

This repository contains the Ruby code to query HST (Hubble Space Telescope) availability information using SOAP protocol and a WSDL file.

gplichoski/squad-3-ad-xp-data-science-1

gplichoski/wanna

💡✔ Wanna is an implementation of a 21st-century to-do list app.