Name: Gabriel Moreira
Type: User
Company: NVIDIA
Bio: ML Research Scientist at NVIDIA focused in Deep Learning for Recommender Systems. Previously Lead Data Scientist, Machine Learning Engineer, Software Engineer.
Twitter: gspmoreira
Location: São José dos Campos, SP, Brazil
Blog: http://about.me/gspmoreira
Gabriel Moreira's Projects
An useless game for Android where player places "angry birds" that attacks a green pig. Just a POC using ArcGIS Runtime for Android.
Experimenting with Benford's Law
Source code of CHAMELEON - A Deep Learning Meta-Architecture for News Recommender Systems
Samples for Google Cloud Machine Learning Engine
Crab is a flexible, fast recommender engine for Python that integrates classic information filtering recommendation algorithms in the world of scientific Python packages (numpy, scipy, matplotlib).
Pure Python and Java compatible implementions of Bloom Filter
Public repository for course materials for the Spring 2013 session of Introduction to Data Science, an online coursera course.
Criteo/Kaggle Competition of CTR prediction
Some examples, prototypes and experiments on Large Language Models SDKs / APIs
A simple Android app with inspiring advices, for any situation, extracted from Holy Bible
HW 3 skeleton for doing BDD with RottenPotatoes
Mirror of Apache TinkerPop (Incubating)
jQuery JavaScript Library
A POC of Google's Wide & Deep Learning models deployed on Google Cloud ML Engine for Kaggle's Outbrain Click Competition
A .NET toolkit to ease creation of Kinect controled WPF map applications (ESRI ArcGIS Runtime for WPF and Telerik Map Control)
K-means on Map Reduce implementation (Python) for thunders locations clustering on South hemisphere
A List of Recommender Systems and Resources
Information for setting up for the BerkeleyX Spark Intro MOOC, and lab assignments for the course
A .NET toolkit to ease creation of Natural User Interfaces (using sensor like Kinect and Leap Motion) to control WPF map applications (like ESRI ArcGIS Runtime for WPF and Telerik RadMap Control)
NVTabular is a feature engineering and preprocessing library for tabular data designed to quickly and easily manipulate terabyte scale datasets used to train deep learning based recommender systems.
This repo demonstrates how Generative AI and Google APIs can be used to compose blog posts about a photo album
PythonForDataScience
This is the first application for Ruby on Rails Tutorial: Learn Rails by Example (http://railstutorial.org/)
Ruby on Rails tutorial demo application
Sample app developed according to railstutorial for practicing
Computation using data flow graphs for scalable machine learning