neural networks

ML: Predict sequence of values

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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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First experiments with ML using Keras

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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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Игры с neural style transfer

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Последний месяц для саморазвлечения в качестве сайд-проекта играюсь с neural style transfer - нейронные сети, переносящие стиль с картинки на картинку. Многие знают это как приложение Prisma. Видимо создатели тоже читают блог creativeai :)

Первая реализация появилась больше 8 месяцев назад и доступна на github jcjohnson/neural-style - она написанна на lua, всё замечательно, кроме того что работает она вечность (и жрёт почти бесконечность памяти). На Digital Ocean с 16 swap генерация картинки 450x200 заняла около 2 минут, сейчас уже не помню.

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