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plugins

Serverless Plugins – Extend the Serverless Framework with these community driven plugins –

rancher-lets-encrypt

Automatically create and manage certificates in Rancher using Let's Encrypt webroot verification via a minimal service

react-exercises

reactive-microservices

under construction - to showcase CQRS and Reative microservices with Reactor

recaptcha4j

added support to locale on google url for personal use

restgraph

Simple Rest api with angularjs and neo4j (in 20 minutes)

rubentrancoso.github.io

seed

First contact with seed - a simple project to experiment it

serverless-cloudformation-changesets

Natively deploy to CloudFormation via Change sets, instead of directly. Allowing you to queue changes, and safely require escalated roles for final deployment.

ses-auto-forward-by-language

shoppingcart

Shopping Cart Proof of Concept

springboot-statemachine

stacks

docker compose stacks

toolbox

algorithms to exercise and learn

udacity-mlaif-bostonhousing

Boston Housing project for Machine Learning & AI Foundations course from Udacity

udacity-mlend-customer_segments

Creating Customer Segments - In this project you will apply unsupervised learning techniques on product spending data collected for customers of a wholesale distributor in Lisbon, Portugal to identify customer segments hidden in the data. You will first explore the data by selecting a small subset to sample and determine if any product categories highly correlate with one another. Afterwards, you will preprocess the data by scaling each product category and then identifying (and removing) unwanted outliers. With the good, clean customer spending data, you will apply PCA transformations to the data and implement clustering algorithms to segment the transformed customer data. Finally, you will compare the segmentation found with an additional labeling and consider ways this information could assist the wholesale distributor with future service changes.

udacity-mlend-finding_donors

CharityML is a fictitious charity organization located in the heart of Silicon Valley that was established to provide financial support for people eager to learn machine learning. After nearly 32,000 letters were sent to people in the community, CharityML determined that every donation they received came from someone that was making more than $50,000 annually. To expand their potential donor base, CharityML has decided to send letters to residents of California, but to only those most likely to donate to the charity. With nearly 15 million working Californians, CharityML has brought you on board to help build an algorithm to best identify potential donors and reduce overhead cost of sending mail. Your goal will be evaluate and optimize several different supervised learners to determine which algorithm will provide the highest donation yield while also reducing the total number of letters being sent.

vanhackfair2-0

webcamcapture

writing