US trip: observations about language

I recently visited USA(California) and I want to share some observations about language
Just a reminder – I’m not a native speaker but grew up on American English, thanks to videos, forums, music and podcasts. Nevertheless I’m living in UK and as you can guess English here is a bit different.

During the trip I collected short list of different words which are common in American but not in British English:

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RPI Zero scan button

While I was finishing wireless scanner and printer server I realised that traditional document scanning approach is not so nice from UX point of view.

I really like the way office scanners in multi-functional devices work. Normally if you want to scan you just load stack of paper into and put your email address. Scanner does the rest and in minute you’ll get ready-to-use pdf file in your inbox.

I was thinking about having button attached to RPI Zero which initiates scanning and document upload.

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RPI Zero: print & scan servers

I had to make old printer and scanner wirelessly available over local network. It can be done using wireless printer USB adapter but it’s not that cheap and still doesn’t support scanner

Obvious choose is using Raspberry PI with linux installed. Before I heard a lot about RPI Zero but could never believe that it costs just £5. Actually it is just £5 and +£2.5 delivery fee. Surprisingly there are no other options except of first class delivery!

Long story short, I put here main steps how to setup print and scan servers on small Raspberry PI Zero:

General configuration

Upgrade RPI software
To access most recent features and freshly created bugs make sure you use most recent version of RPI firmware. To do so run sudo apt-get dist-upgrade.

Static IP
Assign static IP for your raspberry. The easiest way is configure your router DHCP server. Just bind mac address to nice IP in your network, like 192.168.100.100, later in this post I

Reduce graphic memory fraction
If you aren’t going to use video how to

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ML: Predict sequence of values

Let’s say we have nice a line built up of two damped oscillators as it displayed on the picture.

What if I say it’s possible to predict 700 values of this line using just:

  • 30 data points for feeding neural network(which is just half-period)
  • using just two fully-connected layers (hence it’s not deep network)
  • having just three neurons in whole network

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Amazon Alexa Skill: London Bus Stop

Do you prefer to use warm, quiet, spacious bus? Me too
Every time I’m leaving home I have to get updates about time when next busses come to my bus stop. For that reason I literally keep TFL tab open all the time in my mobile

I’ve got freelance job to write Alexa Skill so I decided to play around with pet project. It’s London Bus Stop – custom Alexa Skill which helps user to get information about busses coming to saved bus stop.

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Итог 2016

2016 год вышел сложным и судя по всему не только у меня. Вообще в конце года появился какой то тренд в этих ваших интернетах собирать что было плохого в уходящем году. Но какие-то позитивные итоги подвести все таки надо – еще один год позади как никак

Шутка для нердов про сложный 2016 год:

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Spanish: 200 дней с duolingo

В феврале этого года я взялся за испанский с помощью duolingo. Если кто то вдруг не знает – это сервис для изучения языков. Я бы сказал он именно для

  1. начала изучения – когда человек ничего вообще не знает. Так сильно проще выучить базовые слова, выражения, послушать и как то промямлить фразы, научиться комбинировать всё это вместе.
  2. поддержания ритма занятия – каждый день зелёная сова напоминает тебе выполнить свой минимум. Они считают streak – количество дней которые подряд занимался. Ладно 7 дней жалко потерять, а 30? 100? И всё, просто нет прощения. Где бы ты не был, чем бы не занимался – до 23.40 будь добр найди интернет и сделай минимум.

Помимо этого на дуо очень забавные фразы и дискуссии, читатели моего твиттера наверное уже устали от скриншотов с дурацими фразами в духе “My sister thinks she is normal, but I do not / Mi hermana pequeña piensa que es normal, pero yo no” – зато очень легко запоминается

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Udacity: Machine Learning for Trading

Recently I’m interested in ML in terms of trading and I found course on Udacity platform which dedicated exactly for this topic.

Course structured as 3 big chunks:

1) Data reading and normalisation, vector algebra, regression using python(numpy, pandas) – mostly practice-focused
2) Market mechanics is for whom like me who doesn’t know anything how trading works. Module covers market data and some market indicators like Daily Return, Moving Average, Bollinger Bands, etc. It’s less practical but it’s still possible to code all formulas provided
3) Machine Learning overview. This module mostly about ML approaches, mostly focused on Q-Learning. It’s mostly theoretical, hence you have to go deep into details of implementation yourself. But yet it gives you sense of variety of options you’ve got.

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First experiments with ML using Keras

I’m still exploring the new world of machine learning.

Recently I discovered few interesting things about it:

  • It’s not only for math nerds. A lot of tools are on market, you just have to understand how to use them
  • There are a lot of application of ML, it might be hard to see that from the first sight but it because it’s a completely new way of solving problems you probably never thought about
  • Investors LOVE it, any project which has AI/ML in the description make it more attractive for investments
  • It’s the next Big Thing as you might see already
  • A lot of human’s mechanic work can be replaced with AI
  • In terms of scientific researches I see two kind of them: 1) Very deep math used for building ML tools 2) Adaptation of existing tools, model configurations, best practices for new applications

Hence, I started experimenting with it. First of them was Prisma-like chat bot for Facebook which uses open source implementation of neural style transfer algorithm. Then I made AI-powered visa sponsored job search engine which discovers companies’ websites and walk over job listings to extract vacancies. It uses CNN for binary classification to recognise job listings.

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