Why I Became an Estonian e-Resident

Tallinn, The Capital of Estonia. Source: Crucero Baltico

Coming of Age

Shortly after my 19th birthday, I realized that I would be graduating from university soon with a software engineering degree and would be entering the “real world”. I spent a lot of time thinking about what this “real world” would look like when I graduated and what places would provide the most opportunities for a better future. I have always been very curious about things happening around me and what the world will look like in the next 5, 25, 50 years.

Read More...

How I got interviews at Google, Facebook, and Bridgewater

In the summer after 3rd year, I decided that I wanted to work at a top tech company such as Google or Bridgewater. There was problem though. I didn’t go to a target school, my grades were just okay and my work experience consisted of being a janitor and IT Tech support. I knew if I wanted to get a chance at these top companies I would have to get creative. Read More...

Phlock, My Hardware Startup That Disappeared

 

Phlock 1.0, The beginning of the journey

An early Phlock Prototype.
An early Phlock Prototype

Prior to starting Atila.ca, my previous startup was a company called Phlock. A device that let you unlock doors using your phone and share keys with friends in real time. Phlock didn’t actually fail, it did something much worse, it simply disappeared.

Read More...

Why We Chose Angular over React and Django Over Ruby on Rails for Atila.ca: How to Choose A Software Startup Tech Stack

Atila’s Software Tech Stack

At Atila, we’re trying to provide all students with access to funding for a quality education. This is a big problem with many moving parts and the challenges can be very complex. This article will give you an idea of the software stack we use to solve this problem and some advice on how to choose a software stack for your company as well.

Read More...

The Austrian Quant: My Machine Learning Trading Algorithm Outperformed SP500 For 10 Years

Permanent Portfolio Fund on Quantopian : January 1, 2006 until June 2, 2017

 

Introduction

Dutch Golden Age. Origins of Tulip Mania, The first investment bubble. 17th Century.
Source: Jan Claesz Rietschoof [Public domain], via Wikimedia Commons

Broadly speaking, I generally spend most of my time thinking about two things, technology and investing. More specifically, I often ask myself what is something useful I can build with software (or occasionally hardware) and what is something useful which I should invest in. Algorithmic trading is a nice integration of these two schools and I have been spending some time understanding this field. This is an intriguing field and I learnt some interesting things which I decided to share.

Read More...

Practical Machine Learning

Prologue

For my summer internship, my project involves using machine learning to help small businesses with funding. I learned a lot about machine learning in the process, so I gave a talk about it to some of my co-workers and shared the slides online:

I also shared the code on my Github. The following is an essay version of the talk.

Introduction

Before I say anything, I want to show you a video from the 2017 WWDC Apple conference, WWDC is the annual conference which Apple hosts and is one of the most important events in the tech calendar for showcasing the top technology applications that will be used in the near future.

//platform.twitter.com/widgets.js

So the machine learning supercut gives some context to how I think society generally views machine learning. On the one hand, it’s a technology which has a lot of potential and will drastically change aspects of our society. Conversely, because it has so much potential people have a tendency to over promise and over-advertise the things which machine learning is capable of doing and often turn it into a marketing gimmick and annoying buzzword. For a high level, non-technical summary of what machine learning is about and what the future of technology in general, I recommend Homo Sapiens by Yuval Noah Harari.

I take a more middle-ground approach and say that you should judge it on the merits of what you can actually build with machine learning, but first, you have to understand what machine learning is.

(Guage audience level) How many of you: has never coded before… used ML in a small side project … Studied ML at a Master’s or Ph.D. Level, written or helped write a paper about ML etc.)I’ve tried to structure my talk in such a way that non-technical people will find it interesting and the more technical, ML-experienced people may some new, interesting concepts.

The reason my talk is called practical machine learning is because I consider myself a very pragmatic, practical person and whenever I learn something, the first thing I ask myself is how can I apply what I’ve learned and put it into practice. Hopefully, after today’s talk, you will hopefully be able to apply what you’ve learned and build actual ML projects. Alright, so let’s get started

What is Machine Learning?

My talk was greatly inspired by 2 tutorials which I did, the website is awesome and the guy who runs it Harrison Kinley is a very good teacher.

https://pythonprogramming.net/machine-learning-tutorial-python-introduction/

https://pythonprogramming.net/machine-learning-python-sklearn-intro/

  • ML is a software program that can learn from given inputs and give you the desired output, without explicitly teaching it what you want the out output to be.
  • Google gets approximately .00000003% 1)I forget the exact number, but I’m just repeating what Scott Galloway said better each time you use it
  • In the past, people used to say that the fundamental difference between humans and computers was that humans get better at a task with more information/ “experience” which is something computer can’t do. ML changes that by allowing algorithms/ programs to become more accurate with more information.
    • Also, speaks to a pattern of whenever people say “Computers will never be able to do X because they need a certain skill that only humans have”. It’s usually just a matter of time before computers acquire those skills.
    • Read More...

  • References   [ + ]

    1. I forget the exact number, but I’m just repeating what Scott Galloway said

    Hedged Optimism: Why I Invested in Botswana

     

    Okavango Delta, Botswana. Credit: andbeyond.com

    When I think about the next five, 25, 50 years I often ask myself, “What things will be better in the future? What things will be worse? And how do I best prepare?” I tend to oscillate between excitement-hopefulness and mild paranoia. I have now decided to split the difference between optimism and pessimism and say that I will be Optimistic but Hedged.
    Read More...

    VeroBlue Farms: Betting the Ranch

    Veroblue Farms, Iowa.
    Veroblue Farms, Iowa. Photos by Ackerman + Gruber

    The world population is expected to reach 9.6 billion by mid-2050. Countries with lower standards of living typically have higher population growth rates. The Food and Agriculture Organization of the United Nations (FAO) estimates food production will need to double in some parts of the world by 2050. The confluence of these three factors raises the question of how are we going to feed these many people.

    Read More...

    Thoughts on Money

    "You shall not press down upon the brow of labor this crown of thorns; you shall not crucify mankind upon a cross of gold." - Wiliam Jennings Bryan Cross of Gold Speech, 1896 [wikimedia.org]
    “You shall not press down upon the brow of labor this crown of thorns; you shall not crucify mankind upon a cross of gold.” – Wiliam Jennings Bryan Cross of Gold Speech, 1896 [wikimedia.org]
    Fed chair Janet Yellen with former Chairmen Paul Volker, Alan Greenspan and Ben Bernanke, listen to remarks during the Federal Reserve centennial commemoration, 2013
    Fed chair Janet Yellen with former Chairmen Paul Volker, Alan Greenspan, and Ben Bernanke, listen to remarks during the Federal Reserve centennial commemoration, 2013.[npr.org]
    In order to prevent myself from becoming rapacious, I try to reduce the amount of time I spend thinking about money. However, recent developments have forced me to spend a lot of time understanding exactly why money has been behaving so strangely in recent times.  Read More...

    Swimming Upstream: Nigeria’s Economy Defies All Logic

    A Nigerian oil dealer pours gasoline into bottles at a roadside market in the commercial capital of Lagos in this October 31, 2008 file photo. REUTERS/Akintunde Akinleye/Files
    A Nigerian oil dealer pours gasoline into bottles at a roadside market in the commercial capital of Lagos in this October 31, 2008 file photo. REUTERS/Akintunde Akinleye/Files

    Nigeria’s petroleum industry is very peculiar. Despite producing 1.75 million barrels of crude oil per day(b/d), they only refine about 24,000 barrels into gasoline, leaving them 384,000 barrels short of meeting daily domestic demand.  

    Read More...