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Ongoing observations by End Point people

Job opening: .NET/​C# and JavaScript developer

By Jon Jensen
August 30, 2018

programmer at keyboard on desk
Photo by #WOCinTech Chat · CC BY 2.0, modified

We are looking for another talented full-time software developer to join us, working with .NET and C# on Windows, JavaScript, and at least one of PHP, Java, or Ruby.

If you like to solve business problems and can take responsibility for getting a job done well without intensive oversight, please read on!

End Point is a 23-year-old web consulting company based in New York City, with about 50 employees, the majority working remotely from home offices. We collaborate using SSH and Remote Desktop, GitLab, GitHub, chat, Hangouts and other conferencing software, and good old email and phones.

We serve over 200 clients ranging from small family businesses to large corporations.

We are experts in web development, databases, and DevOps, and we use mostly open source frameworks, in a variety of languages, on Linux and Windows.

What you will need:

  • Professional experience building reliable back-end web applications using .NET and C#, as well as PHP, Java (or Kotlin or Scala), or Ruby
  • Good front-end web skills with responsive design using HTML, CSS, and JavaScript (including jQuery, Vue, Angular, React, etc.)
  • Experience with databases such as PostgreSQL, MySQL, SQL Server, Redis, Elasticsearch, etc.
  • A focus on needs of our clients and their users
  • Strong verbal and written communication skills
  • A bit of experience with cloud services at Microsoft Azure, Amazon Web Services (AWS), or Google Cloud

What you will be doing:

  • Work from your home office, or from our offices in New York City and the Tennessee Tri-Cities area
  • Consult with clients to determine their business application needs
  • Build, test, release, and maintain web applications
  • Work with open source libraries and contribute back as opportunity arises
  • Use your desktop platform of choice, as long as you can get the work done on it: Windows, macOS, Linux
  • Learn and put to use new technologies
  • Direct much of your own work

What does working here bring employees?

  • Flexible...

company jobs

Self-driving toy car using the Asynchronous Advantage Actor-Critic algorithm

By Kamil Ciemniewski
August 29, 2018

The field of Reinforcement Learning has seen a lot of great improvement in the past years. Researchers at universities and companies like Deep Mind have been developing new and better ways to train intelligent, artificial agents to solve more and more difficult tasks. The algorithms being developed are requiring less time to train. They also are making the training much more stable.

This article is about an algorithm that’s one of the most cited lately: A3C — Asynchronous Advantage Actor-Critic.

As the subject is both wide and deep, I’m assuming the reader has the relevant background mastered already. Although reading it might be interesting even without understanding most of the notions in use, having a good grasp of them will help you get the most out of it.

Because we’re looking at the Deep Reinforcement Learning, the obvious requirement is to be acquainted with the neural networks. I’m also using different notions known in the field of Reinforcement Learning overall like $Q(a, s)$ and $V(s)$ functions or the n-step return. The mathematical expressions, in particular, are given assuming that the reader already knows what the symbols stand for. Some notions known from other families of RL algorithms are being touched on as well (e.g. experience replay) — to contrast them with the A3C way of solving the same kind of problems. The article along with the source code uses the OpenAI gym, Python, and PyTorch among other Python-related libraries.

Theory

The A3C algorithm is a part of the greater class of RL algorithms called Policy Gradients.

In this approach, we’re creating a model that approximates the action-choosing policy itself.

Let’s contrast it with value iteration, the goal of which is to learn the value function and have policy emerge as the function that chooses an action transitioning to the state of the greatest value.

With the...


python machine-learning artificial-intelligence

Liquid Galaxy at Google for Nigeria

By Brian Zenone
August 6, 2018

Google for Nigeria

Over the years, Liquid Galaxy has taken us to many a far-flung locale, but few of us at End Point were expecting a Liquid Galaxy installation in Africa. Yet that is exactly what happened: In conjunction with Google’s “Next Billion Users” initiative, End Point was invited to install a Liquid Galaxy at “Google for Nigeria” in Lagos, Africa’s largest city, a teeming metropolis of 21 million souls.

With the challenge of getting our equipment to Nigeria and hearsay about intermittent Internet and electricity, we had more than our usual concern about making sure our Liquid Galaxy would function reliably for the event. And given the US Department of State’s warnings about health and safety, the trip would be more involved than most. However, End Point engineer Will Plaut fairly leapt at the chance. More than 20 hours of flying, a bevy of vaccines and a host of visa procedures were no match for Will’s intrepid spirit and sense of adventure — he even took it upon himself to hunt down the difficult-to-find Yellow Fever vaccine in Greenville, South Carolina.

But before simply sending Will off to fend for himself amidst the hubbub of Lagos, the list of logistics problems seemed unending. The first order of business was to find a local contact to advise us both on the vagaries of shipping and customs as well as to help our engineer set the system up, keep him out of harm’s way, and give him a real taste of Nigeria. A tall order. Luckily, End Point had a personal contact who put us in touch with Olufunmilola “Afeez” Atiku, fixer extraordinaire. Afeez was not only able to explain to us the protocol for shipping (the LG could not be simply shipped to the airport in Lagos, then picked up, but had to be entrusted to a local shipper), he also arranged for Will to be picked up at the airport, whisked through customs, and accompanied & assisted 24×7 for the next 6 days by the joyous and trusted Taofik. The driving chores were delegated to an aptly named fellow known only as “Happiness...


liquid-galaxy event

Meet the End Point Windows consulting group

By Chris Hopkins
July 27, 2018

As the share of internet traffic passing through mobile devices hovers around 50%, it’s easy to forget that a lot of computing still happens on desktops and laptops—​and perhaps even easier to forget that the majority of those desktops and laptops are running Microsoft Windows.

At End Point, we take pride in being a multi-platform organization and using open source technologies in real-world business environments. Our internal chat service, wiki, and many other libraries and tools we employ across our client base are all open source. However, for various reasons many companies choose Microsoft’s solutions including long-time standards Windows Server, Active Directory, and Exchange.

With the acquisition of Series Digital in 2017 and the addition of Dan Briones to our staff, End Point has begun supporting Windows infrastructure in a new way. In 2018 we’ve recently added two new engineers to the mix, Chris Hopkins and Charles Chang, to form a full-service Windows consulting team, all local to our New York City office on Park Avenue.

Dan Briones

Dan Briones

Dan is the team lead and has over three decades of hands-on experience in IT systems management, systems integrations, migrations, virtualization, networking, security, orchestration, security, compliance, and maintenance, specializing in the Microsoft Windows ecosystem. He also develops applications using the .NET framework and SQL Server database. Dan works with many of our local New York clients, focusing on their network and infrastructure.

Charles Chang

Charles Chang

Charles has 21 years of experience in IT ranging from managing Windows infrastructure, to datacenter relocation and hardware refresh initiatives, office relocation, virtualization, disaster recovery and business continuity strategy. Charles has worked extensively with major vendors such as VMware, IBM, EMC, Cisco, and Dell in building enterprise environments in the datacenter. He also has experience in implementing data loss prevention technology, web and email filtering...


windows integration company

Recommender System via a Simple Matrix Factorization

By Kamil Ciemniewski
July 17, 2018

people sitting and laughing
Photo by Michael Cartwright, CC BY-SA 2.0, cropped

We all like how apps like Spotify or Last.fm can recommend us a song that feels so much like our taste. Being able to recommend an item to a user is very important for keeping and expanding the user base.

In this article I’ll present an overview of building a recommendation system. The approach here is quite basic. It’s grounded though in a valid and battle-tested theory. I’ll show you how to put this theory into practice by coding it in Python with the help of MXNet.

Kinds of recommenders

The general setup of the content recommendation challenge is that we have users and items. The task is to recommend items to a particular user.

There are two distinct approaches to recommending content:

  1. Content based filtering
  2. Collaborative filtering

The first one bases its outputs on the the intricate features of the item and how they relate to the user itself. The latter one uses the information about the way other, similar users rank the items. More elaborate systems base their work on both. Such systems are called hybrid recommender systems.

This article is going to focus on collaborative filtering only.

A bit of theory: matrix factorization

In the simplest terms, we can represent interactions between users and items with a matrix:

item1 item2 item3
user1 -1 - 0.6
user2 - 0.95 -0.1
user3 0.5 - 0.8

In the above case users can rate items on the scale of <-1, 1>. Notice that in reality it’s most likely that users will not rate everything. The missing ratings are represented with the dash: -.

Just by looking at the above table, we know that no amount of math is going to change the fact that user1 completely dislikes item1. The same goes for user2 liking item2 a lot. The ratings we already have make up for a fairly easy set of items to propose. The goal of a recommender is not to propose the items users know already though. We want to predict which of the “dashes” from the table are most likely to be liked the most. Putting it in...


python machine-learning

Currency exchange rates with exchangeratesapi.io

By Jon Jensen
July 14, 2018

city street with currency exchange signs

Several of our clients run ecommerce sites, built on custom software, that allow customers to pay in their choice of a few different currencies.

Some have set up separate merchant bank accounts for each currency and use separate payment gateway accounts to accept payments natively in each currency. But more commonly they use a payment gateway that allows them to accept payment in several currencies, but receive funds converted into their specified native currency in their merchant bank account.

In either case, it is common to store prices for products, shipping, etc. in one base currency (say, USD for companies based in the U.S.) and dynamically convert prices for customers. Non-native prices may need to be marked up to cover the cost of conversion into the native currency, depending on the terms of the agreement with the payment gateway or bank.

Because currency exchange rates change often, and because payment gateways generally do not offer a way to retrieve the exchange rates in advance, we need our own source for ongoing retrieval and storage of our exchange rates (also known as forex rates).

For a while we were very pleased with Fixer.io, which was a free service that collected exchange rates from the European Central Bank (ECB) and provided current or historical rates via a simple JSON API. We were sad to find that in March 2018 they deprecated that API and in June 2018 they discontinued it entirely, as described to their users. Fixer.io has transitioned to a paid service and they appear to have improved their operation to retrieve exchange rate data from at least a dozen more sources than the ECB, and to store far more frequent rate updates. Those are nice features, but not something our clients need.

Fixer.io still offers a free plan that allows 1000 API calls per month but in that plan the exchange rates are all based on the Euro. You need to sign up for at least the $10/month plan to retrieve exchange rates from the other currencies as base. As our...


saas ecommerce

Vue, Font Awesome, and Facebook/​Twitter Icons

By David Christensen
July 12, 2018

some Font Awesome fonts

Overview

Font Awesome and Vue are both great technologies. Here I detail overcoming some issues when trying to get the Facebook and Twitter icons working when using the vue-fontawesome bindings in the hopes of saving others future debugging time.

Detail

Recently, I was working with the vue-fontawesome tools, which have recently been updated to version 5 of Font Awesome. A quick installation recipe:

$ yarn add @fortawesome/fontawesome
$ yarn add @fortawesome/fontawesome-svg-core
$ yarn add @fortawesome/free-solid-svg-icons
$ yarn add @fortawesome/free-brands-svg-icons
$ yarn add @fortawesome/vue-fontawesome

A best practice when using Font Awesome is to import only the icons you need for your specific project instead of the thousand+, as this just contributes to project bloat. So in our main.js file, we import them like so:

// Font Awesome-related initialization
import { library } from '@fortawesome/fontawesome-svg-core'
import { faEnvelope, faUser } from '@fortawesome/free-solid-svg-icons'
import { faFacebook, faTwitter } from '@fortawesome/free-brands-svg-icons'
import { FontAwesomeIcon } from '@fortawesome/vue-fontawesome'

// Add the specific imported icons
library.add(faEnvelope)
library.add(faUser)
library.add(faFacebook)
library.add(faTwitter)

// Enable the FontAwesomeIcon component globally
Vue.component('font-awesome-icon', FontAwesomeIcon)

This allows you to include icons in your view components like so:

<template>
  <div class="icons">
    <font-awesome-icon icon="user"/>
    <font-awesome-icon icon="envelope"/>
  </div>
</template>

This worked fine for me until I tried to use the facebook and twitter icon:

<template>
  <div class="icons">
    <font-awesome-icon icon="user"/>
    <font-awesome-icon icon="envelope"/>
    <font-awesome-icon icon="twitter"/>  <!-- broken -->
    <font-awesome-icon icon="facebook"/> <!-- broken -->
  </div>
</template>

Only blank spots and errors in the browser console like so:

[Error] Could not find one or...

vue javascript

Training Tesseract 4 models from real images

By Kamil Ciemniewski
July 9, 2018

table of ancient alphabets

Over the years, Tesseract has been one of the most popular open source optical character recognition (OCR) solutions. It provides ready-to-use models for recognizing text in many languages. Currently there are 124 models that are available to be downloaded and used.

Not too long ago, the project moved in the direction of using more modern machine-learning approaches and is now using artificial neural networks.

For some people, this move meant a lot of confusion when they wanted to train their own models. This blog post tries to explain the process of turning scans of images with textual ground-truth data into models that are ready to be used.

Tesseract pre-trained models

You can download the pre-created ones designed to be fast and consume less memory, as well as the ones requiring more in terms of resources but giving a better accuracy.

Pre-trained models have been created using the images with text artificially rendered using a huge corpus of text coming from the web. The text was rendered using different fonts. The project’s wiki states that:

For Latin-based languages, the existing model data provided has been trained on about 400000 textlines spanning about 4500 fonts. For other scripts, not so many fonts are available, but they have still been trained on a similar number of textlines.

Training a new model from scratch

Before diving in, there are a couple of broader aspects you need to know:

  • The latest Tesseract uses artificial neural networks based models (they differ totally from the older approach)
  • You might want to get familiar with how neural networks work and how their different types of layers can be used and what you can expect of them
  • It’s definitely a bonus to read about the “Connectionist Temporal Classification”, explained brilliantly at Sequence Modeling with CTC (it’s not mandatory though)

Compiling the training tools

This blog post talks specifically about the latest version 4 of Tesseract. Please make sure that you have that installed...


ruby machine-learning
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