Dmytro successfully combines knowledge in mathematics and practical approaches in the data science area. He has two master's degree (in mathematics, and finance), and he has the ph.D.Cognitive learning exercises dmytro has more than 50 scientific publications. His scientific interest lies in the area of mathematical and complex analysis, theory of probabilities, machine learning and predictive analytics, computer vision and time-series analytics.Cognitive learning exercises

Human resource analytics is addressed to the analysis of any employees related problems in any organization. The most significant problems facing by managers are connected with employee turnover.Cognitive learning exercises turnover numbers were always important for any organization, especially in the case of their continued growth – the more the organization is growing the more these numbers become critical.Cognitive learning exercises we are presenting our solution in the context of HR analytics aimed to notify direct people managers about the risks of their employee leaving.Cognitive learning exercises our data-driven solution with the state-of-art engine that uses machine learning approaches to help managers proactively manages unwanted dismissals.Cognitive learning exercises

Alexandr is a machine learning expert with experience in computer vision and time series analysis, who worked with ukrainian (MAWI, ARVI), russian (mlvch), american (inma AI), and italian (HPA srl) companies.Cognitive learning exercises he is mainly interested in bringing research ideas to production and making value from latest theoretical developments in AI. Currently he is working in MAWI (USA-based company with R&D department in ukraine) on biomedical signal analysis, in particular ECG, and applying machine learning for classical applications like medical diagnostics and developing novel cases as well.Cognitive learning exercises meanwhile, alexandr teaches deep learning seminars in university of verona and writes a popular blog on medium.

For hundreds of years, scientists were developing strong theories and rigorous mathematical models to explain patterns and dependencies in data and processes around us.Cognitive learning exercises today instead of modeling some features of data by ourselves we rely on deep neural networks and they don't let us down. So, the natural question arises: do we really need human experts to describe the world mathematically or let's just let AI do all the work?Cognitive learning exercises

In this talk, we will connect dots between generative neural networks (gans) and mathematical models like odes (ordinary differential equations) for ECG analysis: a classical area driven by pure mathematical models for decades.Cognitive learning exercises we analyze empirically what human experts did and what neural networks have learned by themselves and will try to understand, how close we are to fully rely on AI in ECG analysis and other areas.Cognitive learning exercises download presentation

Serge smertin is full-cycle software engineer focusing on data solutions, security and heterogeneous system integration.Cognitive learning exercises during his career worked on various projects starting from ETL systems for e-commerce industry to large-scale malware forensic analysis platforms for cyber-threat intelligence industry hands on with scripting on perl and bash to distributed services on java and scala.Cognitive learning exercises currently serge is working for payments service provider adyen, where he is leading monitoring stream, which focuses on real-time anomaly detection and decision support systems.Cognitive learning exercises previously he was building data science platform using apache spark and jupyter to scale up data initiatives.

Data science within financial technology industry often deals with various sources of data, most of which are commercially sensitive.Cognitive learning exercises for compliance reasons, in some cases there may be a need to answer questions like where the data of certain machine learning model or performance optimization is coming from - tracking a data lineage.Cognitive learning exercises as we know, bigdata decisions may follow through different stages of ETL, as well as interactive data exploration through tools like python, R and SQL.Cognitive learning exercises in this presentation we’ll focus on tracking data lineage for interactive data exploration tools. A set of techniques would be shown to demonstrate how to audit data journey from code entered in notebook down to levels of execution planning, dataframes, rdds and hadoop’s file formats back to visualizations displayed to data analyst.Cognitive learning exercises

Galina is a former natural language processing engineer of 1touch.Io, platform for advanced data lifecycle management. She has long-term experience of continuous delivery of end-to-end NLP solutions mainly focused on performing multi-lingual analysis for high-load systems helping to enhance, summarize and highlight specific units/properties of the text data.Cognitive learning exercises her field of R&D interests also includes handling database processing, running big data analysis on the large scale and implementing existing NLP workflows described in scientific papers.Cognitive learning exercises today, she is responsible for the development and support of solution-specific NLP module of the product, which is based on both classical natural language processing algorithms and deep learning pipelines.Cognitive learning exercises

A workshop consists of two parts: an introductory speech on the development of state-of-art named entity recognition model, which includes recurrent neural network and sequence-to-sequence conditional random fields layer, and a workshop itself, which is dedicated to the development of end-to-end tensorflow named entity recognition neural network.Cognitive learning exercises the presentation part includes an introduction into training data specifications for DLM product, advances of feature engineering and word embeddings usage/combination, framework-specific peculiarities of appropriate model design, and google cloud ML engine capabilities for running the solution using GPU/TPU.Cognitive learning exercises workshop part can be divided into 5 main steps: