Sergio Botero sergiobuj

A Few Useful Things to Know about Machine Learning

The paper presents some key lessons and "folk wisdom" that machine learning researchers and practitioners have learnt from experience and which are hard to find in textbooks.

1. Learning = Representation + Evaluation + Optimization

All machine learning algorithms have three components:

Representation for a learner is the set if classifiers/functions that can be possibly learnt. This set is called hypothesis space. If a function is not in hypothesis space, it can not be learnt.
Evaluation function tells how good the machine learning model is.
Optimisation is the method to search for the most optimal learning model.

Simple collection of Groovy scripts to help me maintain some Jenkins systems.

See also https://wiki.jenkins-ci.org/display/JENKINS/Jenkins+Script+Console

Please comment & let me know if you have a fork / fixes you'd like to include.

Stealing WiFi

/etc/hosts

This will let you access any google owned site. This includes: youtube, google cache, google translate, google search, gmail, google news, etc.

Install the HTTPS Everywhere extension
Add these rules to your /etc/hosts file

Virtual Hosts on nginx (CSC309)

When hosting our web applications, we often have one public IP address (i.e., an IP address visible to the outside world) using which we want to host multiple web apps. For example, one may wants to host three different web apps respectively for example1.com, example2.com, and example1.com/images on the same machine using a single IP address.

How can we do that? Well, the good news is Internet browsers

General Background and Overview

Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
Models and Issues in Data Stream Systems
Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
[Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t

If you're coming to the Property-Based TDD As If You Meant It Workshop, you will need to bring a laptop with your favourite programming environment, a property-based testing library and, depending on the language, a test framework to run the property-based-tests.

Any other languages or suggestions? Comment below.

.NET (C#, F#, VB)

FsCheck

Python:

tmux shortcuts & cheatsheet

start new:

tmux

start new with session name:

tmux new -s myname

	#!/bin/bash
	#
	# Copyright 2015-present Patrice Jaton.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#

	import hashlib, base64, hmac, json, settings

	def shopify_webhook(f):
	"""
	A decorator thats checks and validates a Shopify Webhook request.
	"""

	def _hmac_is_valid(body, secret, hmac_to_verify):
	hash = hmac.new(body, secret, hashlib.sha256)
	hmac_calculated = base64.b64encode(hash.digest())

	#!/bin/bash
	# A simple script to backup an organization's GitHub repositories.

	#-------------------------------------------------------------------------------
	# NOTES:
	#-------------------------------------------------------------------------------
	# * User @jimklimov (and probably some others called out in the long comment
	# thread below) have modified this script to make it more robust and keep
	# up with various changes in the GitHub API and response format at:
	# https://github.com/jimklimov/github-scripts