Data Fest Belarus 2017

May 13 2017
Минск, Беларусь
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Евгений Бабахин

Data Scientist at Wargaming.net

Федор Червинский

Research Engineer at Яндекс

Алексей Натекин

Лидер at Open Data Science

Артур Кузин

Data Scientist at Avito

Петр Ермаков

Data Scientist at HeadHunter

About event

Topic: IT

Конференция объединила исследователей, инженеров и других IT-специалистов. К примеру, резидент ПВТ - компания "Эймэта" создала приложение "Фэбби", которое позволяет менять фон селфи. Разработчики планируют использовать искусственный интеллект и для создания виртуальной косметики - ее можно будет нанести на лицо с помощью смартфона в режиме реального времени. В рамках конференции эксперты обсуждали и новые области применения техники машинного обучения. Технология может помочь и врачам при диагностике сложных заболеваний. По прогнозам аналитиков, рынок технологий искусственного интеллекта к 2025 году увеличится в 60 раз.

Конференцию Data Fest сегодня посетили более 300 человек. Специалисты могли пообщаться с опытными разработчиками и предложить свои идеи. Мероприятие организовано площадкой Space, объединяющей IT-сообщества, компании и специалистов.

Audience

  • CIO
  • Разработчикам
  • Аналитикам
  • Программистам
  • Студентам технических ВУЗов
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Евгений Бабахин

Data Scientist at Wargaming.net

My path from Data Science newbie to Kaggle Master

Overview of what Kaggle is, its pros and cons for the real life. Summary of competitions process and pipeline to handle them (with example for Kaggle competition: "Two Sigma Connect: Rental Listing Inquiries")

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Федор Червинский

Research Engineer at Яндекс

How to build a self-driving car (in 100 lines of code)

Overview of the technology stack inside a modern autonomous vehicle with focus on perception. Different approaches and problem formulations; deep learning for self-driving cars. Mediated perception: semantic segmentation, object detection, depth estimation. Training data: real and simulated

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Алексей Натекин

Лидер at Open Data Science

Free mic session

The aim of this brief session is to expand your data science connections. It is an excellent opportunity for everybody to present themselves to the audience and say a couple of words on their projects and interests related to data science. The presentation format is a two-minute talk and a one-minute question-answer part.

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Артур Кузин

Data Scientist at Avito

Objects segmentation on satellite images (Kaggle DSTL Contest, 2nd place)

An overview of objects segmentation approaches on satellite images from Kaggle competition (Dstl Satellite Imagery Feature Detection). The speech will be dedicated to hacks and tricks of training and design deep convolutional neural networks collected from top-5 teams.

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Алексей Чернобровов

Director at Jet4Retail

How machine learning helps to find effective prices for goods

Review of approaches to pricing. How it was before and how machine learning will change it. The theory and practice. A few examples of the implementation of this approach at a large online stores.

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Антон Лебедевич

‎Data science engineer at Independent contractor

ARIMA vs long term forecasts

Every textbook that has a time series chapter mentions ARIMA so it became a goto model for forecasting. But in real world it struggles with missing data, public holidays, non-stationarity, several steps ahead predictions. Anton will show better alternatives to ARIMA for long term forecasting.

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Михаил Коробов

Software developer at Scrapinghub

Explain me like I'm 5

ELI5 is a Python library which allows to visualize and debug various Machine Learning models. It has built-in support for several ML frameworks and provides a way to explain black-box models. Mike is ELI5 core developer who will tell us more how this magic works.

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Петр Ермаков

Data Scientist at HeadHunter

From Jupyter notebook to production environment

In his speech he will overview the long way from the proof of concept models to highload production maintenance.


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Виталий Радченко

Data Scientist at OpenDataScience

Sentiment analysis: best practices and challenges

Sentiment analysis is a very interesting task where there are many techniques which work well. We will cover data preprocessing, traditional ML, word- and char-based neural networks. Moreover, you will find out different tricks how to deal with small datasets, dataset absence and transfer learning.

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Organizer

EventSpace.by
https://eventspace.by/

Organizer committee: eventspace.by, 375291270011

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