thiagomarquesrocha,Thiago Marques,github

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.

be_mexico

An app that show all about culture, food, dances and much more in Mexico

classify_an_email_in_categories

Machine Learning system that classify a email in categories

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

disko2

An app to list all places where to find out oxygen in Manaus

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

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

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

nnsidia2

Versao 2

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_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.

predicting_client_situation

Machine Learning system to predict client situation if he is "happy", "sad", "upset"

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

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

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.

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.

spoon-knife

This repo is for demonstration purposes only.

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

tenebris

Um sistema de recomendação híbrido de trabalhos acadêmicos para apoio a pesquisa científica, baseado em componentes de filtragem de informação, foi desenvolvido para Web, utilizando frameworks, tais como, Lucene, Mahout e Angular JS.

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

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