Thiago Marques's repositories
Kaio-machine-learning-human-face-detection
Machine Learning project a case study focused on the interaction with digital characters, using a character called "Kaio", which, based on the automatic detection of facial expressions and classification of emotions, interacts with humans by classifying emotions and imitating expressions
siameseQAT
SiameseQAT, a duplicate bug report detection method that considers not only information on individual bugs, but also collective information from bug clusters. SiameseQAT combines attention mechanisms, which were not previously used in this task, with a novel loss function called Quintet Loss, that considers the centroid of duplicate bug report representation clusters andtheir contextual information.
antizikagame
A Game for Android an example how to buid a game without framework. The game has only one module, the user have to kill the many mosquitos until the end. The game has a ranking with the higher score.
classify_an_email_in_categories
Machine Learning system that classify a email in categories
predicting_boston_housing_prices
Machine Learning Engineer Nanodegree! In this project, you will evaluate the performance and predictive power of a model that has been trained and tested on data collected from homes in suburbs of Boston, Massachusetts. A model trained on this data that is seen as a good fit could then be used to make certain predictions about a home — in particular, its monetary value. This model would prove to be invaluable for someone like a real estate agent who could make use of such information on a daily basis.
quickdraw-doodle-recognition
Recognition of million drawings doodle considering three types of birds: 'duck', 'flamingo' and 'swan'. The project focused on six kind of models: MLP, LSTM, GRU, bi-LSTM, CNN and CNN-D
builtwith.angularjs.org
builtwith.angularjs.org
customer-segments
Machine Learning Engineer Nanodegree! In this project, you will analyze a dataset containing data on various customers' annual spending amounts (reported in monetary units) of diverse product categories for internal structure. One goal of this project is to best describe the variation in the different types of customers that a wholesale distributor interacts with. Doing so would equip the distributor with insight into how to best structure their delivery service to meet the needs of each customer
ENADE-2014
Exemplo de Neural Networks Vs Random Forest nos dados do INEP sobre o ENADE 2014 realizado por alunos de Pedagogia da região Sudeste do Brasil.
estoque-laravel-example
Um projeto de exemplo utilizando o framework Laravel
FraternityHealth
Um aplicativo para viabilizar uma rede solidária de atendimentos entre médicos e pacientes
iMototaxiSocketIO
Demo em Android utilizando Socket.IO para o aplicativo Imototaxi
machine-learning
Content for Udacity's Machine Learning curriculum
map-android
Um exemplo utilizando Google Map API e Android plotando um marcador
mlflow_sandbox
A machine learning project sandbox using MLFlow
mockup
Biblioteca para otimizar a construção de aplicativos em Android
ocean-coffee
Aplicativo para pedir copo de café
ocean-lib
Uma lib Android feita para construir apps em poucas linhas, reúne as melhores soluções em Android, do serviço HTTP ao processamento de Imagens.
predict_if_client_bought
Machine Learning system that predict if a client bought inside a website
predicting_client_situation
Machine Learning system to predict client situation if he is "happy", "sad", "upset"
recommendation_item_based_on_client
Artificial Intelligence Beginner: Introduction to Machine Learning! Machine Learning system to recommend item based on clients
schoolweb
An academic system for registering notes and contact students
smartcab
Machine Learning Engineer Nanodegree! In this project you will apply reinforcement learning techniques for a self-driving agent in a simplified world to aid it in effectively reaching its destinations in the allotted time. You will first investigate the environment the agent operates in by constructing a very basic driving implementation. Once your agent is successful at operating within the environment, you will then identify each possible state the agent can be in when considering such things as traffic lights and oncoming traffic at each intersection. With states identified, you will then implement a Q-Learning algorithm for the self-driving agent to guide the agent towards its destination within the allotted time. Finally, you will improve upon the Q-Learning algorithm to find the best configuration of learning and exploration factors to ensure the self-driving agent is reaching its destinations with consistently positive results.
student-intervention-system
Machine Learning Engineer Nanodegree - Supervised Learning - a model that will predict the likelihood that a given student will pass, quantifying whether an intervention is necessary
titanic_survival_exploration
Machine Learning Engineer Nanodegree - In 1912, the ship RMS Titanic struck an iceberg on its maiden voyage and sank, resulting in the deaths of most of its passengers and crew. In this introductory project, we will explore a subset of the RMS Titanic passenger manifest to determine which features best predict whether someone survived or did not survive
web-service-java-angular
An Web Service Java with Angular, Materialize and Jquery example