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Конференція Data Science UA
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  • Data Science UA Conference

    5th International
    Conference in Kyiv
    2018
    |
    NOVEMBER 24
    |
    KYIV

    DATA SCIENCE UA

    Data Science UA Conference brings together leaders in machine learning,
    analytics, BI, data science, AI for a day-long exploration of how data trans-
    forms the World today and what is going to be tomorrow.

    500
    Participants
    18
    Speakers
    3
    Stages
    10
    Hours of networking

    Stages


    Tech

    Business

    Workshops

    Panel discussion

    Speakers


    Andrii Burlutskyi

    Strategic marketing manager at SMART business

    Keynote: Are you Ready to capture the Artificial Intelligence opportunity?

    Volodymyr Tkachuk

    Head of Edge Research, Ring Ukraine

    Theme: How to deploy Machine Learning algorithm on embedded

    Alexandr Honchar

    AI Solution architect, MAWI

    Theme: Multitask learning: learn more to learn better

    Jevgeni Kabanov

    Chief Product Officer, Taxify
    Theme: Machine Intelligence for Urban Mobility

    Vasyl Mylko

    CEO and Founder, Ingeenee

    Theme: AI solves NP-complete

    Juan Pablo Figueroa

    Senior Data Scientist N-iX, Mercanto
    Theme: How to predict with (very) small data

    Viktor Sakharchuk

    Independent CV/ML R&D professional

    Theme: Confidence Measures For Stereo Vision: an Engineer’s View

    Denis Dovgopoliy

    Founder at GrowthUP Group

    Theme: Features in attracting investors to the early stage AI-startups

    Andy Bosyi

    Founder and CEO of MindCraft.ai

    Theme: Active learning metrics or when to stop label the data

    Dmitriy Solopov

    Business Development Manager Advanced Analytics at SMART business

    Theme: Intelligent advisory mechanism (IAM) to engage customers.

    Taras Hnot

    Senior Data Analyst, SoftServe

    Theme: Сustomers profiling based on psychometric characteristics

    Tatyana Fursova

    Expert of card business сross-sales department, OTP Bank
    Theme: Predictive analysis: the way to get a satisfied customer

    Workshops


    Alexandr Honchar

    AI Solution architect, MAWI

    Workshop: Applied multitask learning with Keras

    Maksym Nechepurenko

    Data Scientist N-iX, Mercanto
    Workshop: TICK stack for Time Series data processing and analysis

    Serhii Savaryn

    Python Engineer, JetSoftPro
    Workshop: TICK stack for Time Series data processing and analysis

    Panel discussion


    Theme: “Data Science: today and the future”.

    Borys Pratsiuk

    Head of R&D, Ciklum

    Panel Discussion

    Denis Dovgopoliy

    Founder at GrowthUP Group

    Panel Discussion

    Dmytro Lavrinenko

    Solutions Architect, SoftServe
    Panel Discussion

    Hosts


    Jane Klepa

    Executive director, 1991 Open Data Incubator

    Daniel Che

    Comandante in “Che – Guerrilla Marketing”

    Program Conference


    Tech Stage
    Business Stage
    Workshops

    9:00 – 10:00
    Registration

    10:00 – 10:10
    Opening speech

    10:10 – 10:40
    Keynote

    Andrii Burlutskyi

    Strategic marketing manager at SMART business
    Keynote: Are you Ready to capture the Artificial Intelligence opportunity?

    10:45 – 11:30

    Vasyl Mylko

    CEO and Founder, Ingeenee
    Theme: AI solves NP-complete

    Jevgeni Kabanov

    Chief Product Officer at Taxify
    Theme: Machine Intelligence for Urban Mobility

    11:30 – 11:50
    Coffee Break

    11:50 – 12:35

    Juan Pablo Figueroa

    Senior Data Scientist N-iX, Mercanto
    Theme: How to predict with (very) small data

    Tatyana Fursova

    Expert of card business сross-sales department, OTP Bank
    Theme: Predictive analysis: the way to get a satisfied customer

    12:45 – 13:30

    Alexandr Honchar

    AI Solution architect, MAWI
    Theme: Multitask learning: learn more to learn better

    Dmitriy Solopov

    Business Development Manager Advanced Analytics at SMART business
    Theme: Intelligent advisory mechanism (IAM) to engage customers.

    11:30 – 13:30

    Maksym Nechepurenko

    Data Scientist N-iX, Mercanto

    Serhii Savaryn

    Python Engineer, JetSoftPro

    13:30 – 14:30
    Lunch

    14:30 – 15:15

    Viktor Sakharchuk

    Independent CV/ML R&D professional
    Theme: Confidence Measures For Stereo Vision: an Engineer’s View

    Taras Hnot

    Senior Data Analyst, SoftServe
    Theme: Сustomers profiling based on psychometric characteristics

    15:25 – 16:10

    Andy Bosyi

    Founder and CEO of MindCraft.ai
    Theme: Active learning metrics or when to stop label the data

    Denis Dovgopoliy

    Founder at GrowthUP Group
    Theme: Features in attracting investors to the early stage AI-startups

    16:10 – 16:30
    Coffee Break

    14:30 – 16:30

    Alexandr Honchar

    AI Solution architect, MAWI

    16:30 – 17:15

    Volodymyr Tkachuk

    Head of Edge Research, Ring Ukraine
    Theme: How to deploy Machine Learning algorithm on embedded

    Speaker to be confirmed soon

    Theme: Info comming soon

    17:30 – 19:00

    Panel discussion

    Theme: Data Science: today and the future
    Borys Pratsiuk — Head of R&D, Ciklum
    Denis Dovgopoliy — Founder at GrowthUP Group
    Dmytro Lavrinenko — Solutions Architect, SoftServe

    Tickets


    Buy 5 tickets Get 10%
    off
    Buy 10 tickets Get 15%
    off
    25% discount for student

    1450
    June 20 – August 31

    1950
    September 1- September 30

    2150
    October 1 – October 31

    2750
    November 1 – November 23

    3000
    November 24

    Become one of our Partners

    The Data Science UA Conference attracts both, the best and brightest attendees,
    and many of the top companies using data science.

    General partner


    Tech Stage partner


    Silver partners


    Bronze partners


    General HR partner


    Book partner


    Water partner


    Media Accreditation

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    Tickets for talented youth

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    Contacts


    Location

    Oasis concert hall Kyiv

    Kiev, Lipkovsky street, 1A
    “Ultramarine”, 3rd floore


    Contact information

    +38 099 055 23 92
    info@data-science.com.ua
    #datascienceua


     

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    Volodymyr Tkachuk

    Head of Edge Research, Ring Ukraine

    Theme: How to deploy Machine Learning algorithm on embedded

    About speaker:
    In 2008, he graduated from NTUU “KPI” in “Applied Mathematics”.
    Worked as a programmer, researcher, researcher, technical manager in such companies as Microsoft (Copenhagen), the Department of Pattern Recognition in Kibtsentr (Kyiv), Materialise NV (Kyiv, Shanghai) and Ring Ukraine (Kyiv).
    I was engaged in projects of 3D reconstruction of a scene on two-dimensional shots, segmentation of a three-dimensional medical image, optimization of 3D printing algorithms.
    The current work in Ring Ukraine is connected with the development and implementation of algorithms to be performed on “smart” devices such as courier calls and Ring’s cameras.
    Briefly about the report:
    Learning complex algorithms, such as large neural networks, is still a resource-intensive procedure that requires complex hardware. At the same time, the emergence of “light” architectures (for example, MobileNet), and the simultaneous increase in the power of embedded systems, creates the ability to perform machine-trained algorithms on “smart” devices.
    There are various ways to optimize and run their algorithms. The easiest way is to use community-supported and developed frameworks such as Mini Caffe and TensorFlow lite. Some manufacturers of built-in systems are equipping their products with chips optimized for neural networks and offering libraries to work with them. Finally, you can always program itself to implement any network in the language C / C ++ with optimization for a particular processor.

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    Denis Dovgopoliy

    Founder at GrowthUP Group

    Theme: Features in attracting investors to the early stage AI-startups

    About speaker:
    He graduated from the Kiev Polytechnic Institute (1996) and International Institute of Management, MBA Program (2000). He is a co-founder of the first Ukrainian business accelerator for IT startups of the GrowthUP.
    One of the leaders of the Ukrainian venture and entrepreneurial community.
    Founder and organizer of several significant industry events: IDCEE, iForum, Silicon Valley Open Doors, Startup Crash Test, etc.
    Before founding BVU Group, he held C-level positions in telecom companies. He founded several local companies.

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    Denis Dovgopoliy

    Founder at GrowthUP Group

    Theme: Features in attracting investors to the early stage AI-startups

    About speaker:
    He graduated from the Kiev Polytechnic Institute (1996) and International Institute of Management, MBA Program (2000). He is a co-founder of the first Ukrainian business accelerator for IT startups of the GrowthUP.
    One of the leaders of the Ukrainian venture and entrepreneurial community.
    Founder and organizer of several significant industry events: IDCEE, iForum, Silicon Valley Open Doors, Startup Crash Test, etc.
    Before founding BVU Group, he held C-level positions in telecom companies. He founded several local companies.

    ×

    Taras Hnot

    Senior Data Analyst, SoftServe

    Theme: Сustomers profiling based on psychometric characteristics

    About speaker:
    Taras is a data analyst with a strong experience in performing large-scale data mining, statistical modeling and patterns extraction. He has been involved into various projects in digital retail industry, including developing analytical tools for a customer profiling and segmentation, building complex personalization and product recommendation solutions and customer behavioural modeling using psychometry and neuroeconomics techniques. Taras is a co-author and an author of multiple scientific and media publications, including “Anomaly Detection – Unsupervised Approach”, “Recommender Systems Comparison: The Best Performing Algorithm” and “Bitcoin Network Analytics”.
    Briefly about the report:
    We will talk about retailers’ customers profiling, but not based on gender, age, occupation, family. Profiling will be based on customers’ psychometric characteristics or OCEAN scores. These scores would be used to show the type of ad that customer will like (transformational or informational/comparative or non-comparative / one or two-sided ads etc.). Psychometric characteristics are extracted from textual information, written by the customer, and shopping patterns using state-of-the-art techniques in machine learning like XGBoost, Random Forest, LSTM models.

    ×

    Alexandr Honchar

    AI Solution architect, MAWI

    Theme: Multitask learning: learn more to learn better

    About speaker:
    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. 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. Meanwhile, Alexandr teaches deep learning seminars in University of Verona and writes a popular blog on Medium.
    Briefly about the report:
    Machine learning as a discipline went through a long way from statistical pattern recognition in clean and tiny data to learning meaningful representations from complex high dimensional data on scale with deep learning algorithms. Indeed, it reminds in a way on how human brains process the information… but not close enough. One of the amazing abilities of human brain is performing several different conclusions from one piece of data – seen a cat, we don’t just identify a class “cat” with 97% of confidence, we also see the color, how far it is from us, try to detect the mood and cuteness of the animal. The same happens when we read text or hear the speech – we understand a lot from read of heard sentences and the same should do deep learning algorithms. In this talk we’ll study the concept of multitask learning – doing several tasks on the same data, understand why it helps machine learning to generalize and understand problem better and review cases in computer vision, NLP, reinforcement learning and other fields so you can apply this idea in your business.

    ×

    Alexandr Honchar

    AI Solution architect, MAWI

    Briefly about the Workshop:
    In this workshop, we will study multitask learning in practice. Multitask learning is a concept of designing machine learning algorithms in such a way, so they learn to solve several problems using the same set of parameters. Not surprisingly it helps to generalize and learn new and more useful features from the data than with using a single task and a single loss function. We will briefly review the theory behind multitask learning but will concentrate on implementing these models (mainly neural networks) by ourselves using Keras and apply it to different datasets (computer vision, signal processing) to prove their effectiveness.

    ×

    Borys Pratsiuk

    Head of R&D, Ciklum Theme

    About speaker:
    Borys graduated with honors from the Chair of Physical and Biomedical Electronics of the KPI in 2007 on the specialty “Physical and Biomedical Electronics”, and in 2012 he defended his dissertation at the Faculty of Electronics in the KPI. Boris Pracyuk works in the Ciklum R&D department. And also has his own startup – Fino (financial assistant).

    ×

    Jane Klepa

    Executive director, 1991 Open Data Incubator

    About Jane:
    Executive Director at 1991 Open Data Incubator, Co-founder & CMO at SPREAD.
    Startups mentor and adviser. Teenagers mentor at ukrainian business schools (Computer academy “Step” and “Creators”). Actively involved in the tech ecosystem development in Kyiv and Ukraine.
    Expert of a corporate social responsibility. Her first exposure to Ukraine’s IT world was when she worked as a PR-manager at tech events. Then moved to work as an organizer of largest tech events (Ukraine, Russia, Poland).

    ×

    Daniel Che

    Comandante in “Che – Guerrilla Marketing”

    About Daniel Che:
    Founder of “CHE-guerrilla marketing”.
    A passionate, futuristic, technocratic dreamer, and evangelist of change.
    “I believe that data science will change the perception of how we live and conceive. Humanity is on the verge of colossal changes, which is why the best we can do is to be surrounded by people who are just dreaming that same way. Those who dream not only to observe changes but also to be their main driving force.
    So, see at Data Science UA Conference 2018.

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    Viktor Sakharchuk

    Independent CV/ML R&D professional

    Theme: Confidence Measures For Stereo Vision: an Engineer’s View

    About speaker:
    Volyn National University – master of science – theoretical physics. Lutsk National Technical University- Ph.D. student. 10+ years of commercial experience as the software/hardware engineer.
    Main skills
    Rapid prototyping and implementation of complex SW/HW solutions for 2D and 3D pattern recognition and object detection from data of different origin, multisensor fusion. Designing environment perception systems for UAV/UGV for their orientation in space. Image processing.
    Applying computer vision and machine learning methods for solving real-life problems.
    Briefly about the report:
    The report will be about the practical aspects of dealing with data obtained from the binocular stereo system. Usually, such data are corrupted by noise and outliers and are rarely used in the raw form. It may lead to incorrect interpretations of the distance measurements and improper reasoning about the scene being observed (phantom obstacles, missing objects etc). Also, it is crucial to obtain the confidence measure to get a reliable sensor fusion system. Therefore estimating a reliable confidence of the data is an important step in the data processing pipeline of every autonomous system
    We will touch the confidence estimation approaches for the data from common stereo disparity estimation algorithms being used in robotic perception systems that work in real-time

    ×

    Vasyl Mylko

    CEO and Founder, Ingeenee

    Theme: AI solves NP-complete

    About speaker:
    Co-created R&D at SoftServe in 2008, and led it as R&D Director for 10 years.
    Recent prominent researches have been done in Emotional Intelligence, Machine Empathy, Biometry, Embedded AI, IoT, AR. Our BioLock authentication by ECG was a finalist in the 2017 SXSW Interactive Innovation Awards
    Co-designed several cutting-edge technologies with/for Samsung since 2016.
    Earlier in R&D: built UX Design Office and Security Consulting
    Briefly about the report:
    Despite chip/memory/storage/networks giant leap since 1970s, a dozen of known optimization problems are still unsolved in short time using cheap resources with acceptable quality. Naming a several of them: Traveling Salesman Problem, Constraint Satisfaction Problem, Protein Folding, Earth Simulation. They are of combinatorial complexity. On the one hand, those problems are attacked with HPC by supercomputers. On the other hand it could be possible to apply new kind of AI, to solve them with cheaper resources. There could be a link between intelligence and computational universe – AI can build complexity.
    At Ingeenee, we are building an ingenious engine, that can find a maximum sightseeing in any geographical area and build the route through the cities with POIs, within time and budget constrainsts simultaneously. I will share stories how we solve this NP-complete problem (TSP+CSP) with ML and Evolutionary Computing.

    ×

    Andy Bosyi

    Founder and CEO of MindCraft.ai

    Theme: Active learning metrics or when to stop label the data

    About speaker:
    Andy has 25 years experience in software development. He built his first Artificial Neural Network in 1986, worked on various IT projects related to big data and data analytics, from 2016 switched on data science projects and set up a company MindCraft fully dedicated to creating data-driven solutions to bring new ideas from data insights and reduce staff costs
    Briefly about the report:
    Most sources are lively talking about machine learning systems, often not taking into account the fact that 80% of the time and energy of analytic goes to data mining, analysis, and labeling.
    Scope big data system it’s a huge expense for low-skilled (and not only) work. Here comes the help of semi-supervised learning methods, which allow one to teach models for partially labeled data
    In the report, we will consider the case when data labeling and learning process go iteratively with maximum efficiency. The main question here is when it is enough to spend resources on data labeling while maintaining the qualitative metrics of the model.

    ×

    Dmitriy Solopov

    Business Development Manager Advanced Analytics at SMART business

    Theme: Intelligent advisory mechanism (IAM) to engage customers.

    About speaker:
    Mission is to serve SMART business customer, using innovations in analytical and cognitive services to increase business effectiveness. Dmitriy spent 7 years developing technology and strategy at Microsoft. And 4 years as certified trainer experienced in readiness programs development, official Microsoft
    courses. Guest speaker at leading conferences: Predictive Analytics World (DE), Dynamics Day, DeForum, SMART talks, Idea Days at KMBS and other.
    Briefly about the report:
    CRM + ERP + POS + loyalty program – what’s next?
    What can you do to increase the average check and consumer basket?
    How to increase margin by automatically accounting price elasticity?
    The story about one intelligent promotion model (IPM) tracking 25 behavioral segments, increasing the check, the frequency of visits and the base of loyal customers in dynamics with accurate recommendations.

    ×

    Andrii Burlutskyi

    Strategic marketing manager at SMART business

    Theme: Are you Ready to capture the Artificial Intelligence opportunity?

    About speaker:
    Digital Transformation Expert and Fan of Our Future, having 14 years’ experience in IT business, 10 of which at Microsoft.
    Keynote speaker at leading conferences: Dynamics Day,SMART talks, iForum, Forum of Marketing Directors, Ideas Days in KMBS, DeForum Microsoft and others.
    His purpose is to build trust to technologies and storytelling
    Briefly about the report:
    AI isn’t just another piece of technology. It could be one of the world’s most fundamental pieces of technology the human race ever created.
    Business, life and the world – what is the AI opportunity? Why is AI Different? What we call AI and what AI is?
    How can we build own practice creating IP product for the market? The importance of creating your own Intellectual Property is crucial as never before as we as ethics in AI.

    ×

    Tatyana Fursova

    Expert of card business сross-sales department, OTP Bank
    Theme: Predictive analysis: the way to get a satisfied customer

    About speaker:
    Tatyana was graduated from Cherkasy State Technological University in 2004 and got her diploma in Computer science, at the same time she received a diploma of the second degree in Accounting and Audit.
    She worked as a leading analyst in such companies as Branan, Readers Digest, Pro-Сonsulting, Nova Posta. She was engaged in market research, forecasting, development of effective marketing and business strategies, behavioral patterns researches and modeling. At the moment she is the leading expert in the field of data analysis and Data Science in OTP Bank engaged in the project for the implementation and development of the Analytical Approach to CRM
    Briefly about the report:
    Development of business-oriented predictive analytics to achieve profit and cost-cutting. Here we will talk about client’s full life-cycle: attraction, development and retention. We will review step-by-step instruction for development of business-oriented concept for predictive models : business process-problem-data understanding- data mart-testing-evaluation-deployment-reporting.

    ×

    Juan Pablo Figueroa

    Senior Data Scientist N-iX, Mercanto
    Theme: How to predict with (very) small data

    About speaker:
    Juan Pablo is a data scientist at N-iX, where he works in projects related to recommendation systems, predictive modeling and time series forecasting. He has over 5 years of experience building predictive models for different industries, including Ad Tech, Healthcare and Retail, among others. Juan Pablo has a background in Statistics and holds a MSc in Machine Learning from University College London, and his most recent area of focus is Automated Machine Learning (AutoML).

    ×

    Maksym Nechepurenko

    Data Scientist N-iX, Mercanto
    Workshop: TICK stack for Time Series data processing and analysis

    About speaker:
    I am a Data Scientist who is enabling data driven decision making by transforming varied and vast into the meaningful and simple. Passionate about data science using cutting-edge technologies to unveil the full power of Big Data Analysis and Machine Learning in order to solve complex Data Science and Business Intelligence tasks across multiple domains and industry sectors. Deep background in Mathematics, Statistics – ‘PhD in Mathematical Physics. Machine Learning experience: Time-Series, Digital Signal Processing, NLP, Anomaly detection, Voice recognition (ASR), dialogue systems, stress analysis, recommender systems etc.
    A professional who helps to structure and leverage your company’s raw data in order to uncover hidden facts, patterns, correlations, and trends that will enable you to make better informed decisions.
    Briefly about the workshop:
    During this workshop, we will build a system for collecting, aggregating, monitoring and analyzing open data of crypto exchangers, and will also monitor the system we have built in real time. We will use the InfluxData Open Source TICK (Telegraf, InfluxDB, Chronograph, Kapacitor) Stack.
    Participants will be able to obtain theoretical and practical knowledge of continuous (stream) and batch processing of time series data using the above mentioned stack.

    ×

    Serhii Savaryn

    Python Engineer, JetSoftPro
    Workshop: TICK stack for Time Series data processing and analysis

    About speaker:
    I have been working as a Python developer for more than 3 years, working on projects connecting with processing, aggregation and analysis of audio data, financial data, measurement devices for water consumption. I am currently working on a project related to crypto currencies and crypto markets.
    Briefly about the workshop:
    During this workshop, we will build a system for collecting, aggregating, monitoring and analyzing open data of crypto exchangers, and will also monitor the system we have built in real time. We will use the InfluxData Open Source TICK (Telegraf, InfluxDB, Chronograph, Kapacitor) Stack.
    Participants will be able to obtain theoretical and practical knowledge of continuous (stream) and batch processing of time series data using the above mentioned stack.

    ×

    Dmytro Lavrinenko

    Solutions Architect, SoftServe

    About speaker:
    Over 13+ years of commercial experience in software development and 6+ years of DevOps experience. Successfully led complex projects with small and middle teams, covering various aspects of software development, process, and methodology.
    Main responsibilities:
    Principal Architecture;
    Specializing in integrations and improvements;
    Consolidating Developers environment and Operations environment;
    Creating, reviewing, improving system architecture
    Participants will be able to obtain theoretical and practical knowledge of continuous (stream) and batch processing of time series data using the above mentioned stack.

    ×

    Jevgeni Kabanov

    Chief Product Officer, Taxify
    Theme: Machine Intelligence for Urban Mobility

    About speaker:
    Jevgeni Kabanov was the founder and CEO of ZeroTurnaround, a development tools company acquired by Rogue Wave. He is an entrepreneurship champion and currently the president of the Estonian Startup Leaders Club. In 2015, he received the EY Entrepreneur of the Year award in Estonia. Jevgeni has a PhD in Computer Science from the University of Tartu and has published papers on topics ranging from category theoretical notions to typesafe Java DSLs. He has initiated several open source projects and serves on the board of a number of startups
    Briefly about the report:
    In Taxify’s Product, Engineering and Data Science teams, we aim to keep the user experience smooth and simple for both our riders and drivers. Our goal is to provide convenient and reasonably priced rides for anyone in need within 3 minutes in all the 40+ cities we operate in.
    At first it doesn’t sound that hard, but behind the scenes there are a lot of interesting problems and challenges we solve on a daily basis. In this presentation, we are going to give you a brief overview of what Taxify’s Product, Engineering and Data Science teams are working on.

    ×

    Workshop: TICK stack for Time Series data processing and analysis

    Briefly about the workshop:
    During this workshop, we will build a system for collecting, aggregating, monitoring and analyzing open data of crypto exchangers, and will also monitor the system we have built in real time. We will use the InfluxData Open Source TICK (Telegraf, InfluxDB, Chronograph, Kapacitor) Stack.
    Participants will be able to obtain theoretical and practical knowledge of continuous (stream) and batch processing of time series data using the above mentioned stack.

    ×

    Workshop: Applied multitask learning with Keras

    Briefly about the Workshop:
    In this workshop, we will study multitask learning in practice. Multitask learning is a concept of designing machine learning algorithms in such a way, so they learn to solve several problems using the same set of parameters. Not surprisingly it helps to generalize and learn new and more useful features from the data than with using a single task and a single loss function. We will briefly review the theory behind multitask learning but will concentrate on implementing these models (mainly neural networks) by ourselves using Keras and apply it to different datasets (computer vision, signal processing) to prove their effectiveness.