Andrew Luetgers andrewluetgers

Awesome Knowledge Graph Embedding Approaches

This list contains repositories of libraries and approaches for knowledge graph embeddings, which are vector representations of entities and relations in a multi-relational directed labelled graph. Licensed under CC0.

Libraries

🆕 AmpliGraph (4 algorithms) @ https://github.com/Accenture/AmpliGraph
Embedding framework (5 algorithms) @ https://github.com/BookmanHan/Embedding

Lodash has a sweet feature called a mixin that lets you alias function names. Below here I alias names that we're used to using in SQL to (roughly) equivalent functions in lodash.

_.mixin({
  select: _.map,
  from: _.chain,
  where: _.filter,
  groupBy: _.sortByOrder,
})

##Using Let's Encrypt certificates with AWS API Gateway

Before starting off with API Gateway set up it's worth mentioning that certificate configuration for this particular service is so far isn't well integrated, therefore different from other AWS services. Despite it using CloudFrount to serve on custom domains it won't let you customize distributions it creates, however all the limitations of CloudFront naturally apply to API Gateway. The most important in this case is the size of the key, which is limited by 2048 bit. Many tutorials provide ready to use terminal commands that have the key size preset at 4096 bit for the sake of better security. This won't work with API Gateway and you'll get an error message about certificate's validity or incorrect chain which won't suggest you the real cause of the issue. Another consideration is that to add a custom domain to API Gateway you have to have a certif

Postgres push triggers

Watch a table for changes and push a notification with a payload describing the change.

Example

In the Postgres shell:

-- Create the functions

	import React from 'react'
	import { observer } from 'mobx-react'

	function ArtistList({ uiState: { artistData } }) {
	const { data } = artistData;
	if ( !data \|\| !data.artists \|\| !data.artists.length ) {
	return <div>No artists bruh.</div>
	}
	const { artists } = data
	return (

	// Shannon entropy
	const entropy = str => {
	return [...new Set(str)]
	.map(chr => {
	return str.match(new RegExp(chr, 'g')).length;
	})
	.reduce((sum, frequency) => {
	let p = frequency / str.length;
	return sum + p * Math.log2(1 / p);
	}, 0);

	CREATE OR REPLACE FUNCTION notify_trigger() RETURNS trigger AS $$
	DECLARE
	channel_name varchar DEFAULT (TG_TABLE_NAME \|\| '_changes');
	BEGIN
	IF TG_OP = 'INSERT' THEN
	PERFORM pg_notify(channel_name, '{"id": "' \|\| NEW.id \|\| '"}');
	RETURN NEW;
	END IF;
	IF TG_OP = 'DELETE' THEN
	PERFORM pg_notify(channel_name, '{"id": "' \|\| OLD.id \|\| '"}');

	'''This script goes along the blog post
	"Building powerful image classification models using very little data"
	from blog.keras.io.
	It uses data that can be downloaded at:
	https://www.kaggle.com/c/dogs-vs-cats/data
	In our setup, we:
	- created a data/ folder
	- created train/ and validation/ subfolders inside data/
	- created cats/ and dogs/ subfolders inside train/ and validation/
	- put the cat pictures index 0-999 in data/train/cats

	/*
	by Martin Güther @magegu

	just call it:

	uploadFile(absoluteFilePath, callback);
	*/

	var path = require('path');
	var async = require('async');