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biaxial-rnn-music-composition

A recurrent neural network designed to generate classical music.

bigml-predictive-app

Video explaining my project: https://drive.google.com/open?id=1RdK2WM6CKtSLO1K1VfK3JUwyf5nl6cZB

buildingmachinelearningsystemswithpython

Source Code for the book Machine Learning Systems with Python

buildingyourown3dprinter

Book about building a RepRap Prusa Mendel 3D printer

calima

Calima - La memoria del clima. Web/API para la serie histórica de datos meteorológicos de AEMET.

carnd-lanelines-p1

Lane Finding Project for Self-Driving Car ND

cartoonify

python app to turn a photograph into a cartoon

cdrd

Simple CDR/SMDR logger.

coco

MS COCO API - http://mscoco.org/

cognitive-samples-videoframeanalysis

A library with sample apps for continuous analysis of live video, using the Microsoft Cognitive Services Vision APIs.

cognitive-video-windows

Windows SDK for the Microsoft Video API, part of Cognitive Services

cognitive-vision-windows

Windows SDK for the Microsoft Computer Vision API, part of Cognitive Services

com.opendoorlogistics

comp-finance

This repository contains Python-based tools for Computational Finance. It is related to the Computational Finance blog run by Stuart Reid (www.stuartreid.co.za/blog).

coursera-ml

Exercises of Machine Learning Course taught by Prof. Andrew Ng on Coursera.

csharp_taclient

A simple C# class to connect to and consume the TripAdvisor Content API

curso_aeropython

Curso de iniciación a Python orientado en la ESTIAE de la UPM

data4people-women-s-health-risk-assessment

Summary In this article, we are going to present the solution for the Women’s Health Risk Assessment data science competition on Microsoft’s Cortana Intelligence platform which was ranked among the top 5%. In this page, you can find the published Azure ML Studio experiment., a description of the data science process used, and finally a link to the R code (in GitHub). Competition Here is the description from the Microsoft Cortana Competition “To help achieve the goal of improving women's reproductive health outcomes in underdeveloped regions, this competition calls for optimized machine learning solutions so that a patient can be accurately categorized into different health risk segments and subgroups. Based on the categories that a patient falls in, healthcare providers can offer an appropriate education and training program to patients. Such customized programs have a better chance to help reduce the reproductive health risk of patients. This dataset used in this competition was collected via survey in 2015 as part of a Bill & Melinda Gates Foundation funded project exploring the wants, needs, and behaviors of women and girls with regards to their sexual and reproductive health in nine geographies. The objective of this machine learning competition is to build machine learning models to assign a young woman subject (15-30 years old) in one of the 9 underdeveloped regions into a risk segment, and a subgroup within the segment.” https://gallery.cortanaintelligence.com/Competition/Womens-Health-Risk-Assessment-1 Dataset The contains 9000 observations The original training dataset is in CSV format and can be found in the competition’s description. To submit a solution, two options are possible: build it in Azure ML Studio or build your solutions locally in R and then submit it through Azure ML Studio. An Azure ML’s solution, and a R script code where given as example. The two solutions are based on the use of a Generalized Linear Model is automatically downloaded. You can find a detailed description of the dataset, the R sample Code and a tutorial using Azure ML and R in the competition page Solution I started following the R tutorial for this competition. Then I have submitted the exact same R solution. The sample model has a 77% accuracy Pre-processing & Cleaning The first thing I did was changing the initial multinomial model (nnet package) for a random forest model (RandomForest package). All missing values have been replaced by 0 Feature selection Features have been selected using the function varImpPlot from the randomforest package Parameter tuning I have chosen (for educational matter) to use the module Tune Model Hyperparameters in Azure ML Studio. I could have also used the R Package Caret. Evaluation The final model has an accuracy of 86.36% (18 position over almost 500 participants) You can download the R code here

dataset

An archive of datasets distributed with R, copied from https://github.com/vincentarelbundock/Rdatasets

deep-learning

This repository contains project files and scripts for Udacity's Deep Learning Course.

deepdreamvideo

implementing deep dream on video

dht-sensor-library

Arduino library for DHT11DHT22, etc Temp & Humidity Sensors

diet-optimization

Given food nutrients data from Public Health England, finds an optimal diet for group members to maximize their preference scores while fulfilling nutrient requirements. The Python scripts generate an lpsolve script file as output that will produce optimized diets.

diogenes

Searching for an honest classifier

django

The Web framework for perfectionists with deadlines.

django-pbx-smdr-accounting

A django project for collecting and reviewing Station Message Detail Record (SMDR) data from Private Branch Exchange (PBX) phone switches. This makes phone call accounting possible.

drive

dvo_slam

Dense Visual Odometry and SLAM

eap-sim

SS7 gateway for requesting authentication info from HLR/AuC

email-osint-ripper

EO-RIPPER.PY es una herramienta que nos permite hacer OSINT con un email o con una lista de emails.