Getting insights from 100 billion DNS transactions per day: challenges of applying data science to big data. Examples of passive DNS analysis discoveries.
Yuriy Yuzifovich, Nominum, San Francisco
Growing data science practice at EPAM: examples and future outlook.
Val Tsitlik, Vice President and the Head of the Big Data Competency Center, EPAM, Washington DC
Fully connected vs convolutional neural nets: machine learning in image processing. Scaling up with high performance computing techniques.
Artem Nikonorov, SSAU, Samara
Frontiers in neuroinformatics: Reverse engineering the brain from fMRI brain scans. Distributed processing pipeline using Apache Spark.
Konstantin Bychenkov, sMedx, Samara
Speeding up data engineering tasks: Scaling data processing pipelines both horizontally and vertically to achieve better performance with Python.
Nikolay Markov, Aligned Research, San Francisco
Alexandr Notchenko, Teatrall, Moscow
Using eXtreme Gradient Boosting (XGBoost) to forecast TV ratings: mixing viewership data with external data sources to improve prediction accuracy.
Dmitry Larko, Data Scientist, EPAM, San Francisco
Convolutional and recurrent neural networks: how it works, how to
unite them, examples and results.
Michail Patkin, Deep learning engineer, Gentleminds, Samara