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  • JUNE 23
    Boris Tarovik
    RnD Engineer, Readdle

    WORKSHOP

    KYIV
    Ivan Budnikov
    Machine Learning Engineer, Readdle

    Data Science at Readdle

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    Workshop: Data Science at Readdle – is a Data Science UA and Readdle project, in which we, together with Ivan Budnikov and Boris Tarovik, will consider aspects of the Data Scientist’s work, the life cycle of the ML project, the basic ML algorithms, examples of neural networks in production of such companies as Google, Readdle, Prisma and more!

    Participants will be able to create simplest ML solution for house price estimating, using sklearn/numpy and a neural network to solve computer vision problem, using tensorflow

    Participation in the workshop is free for pre-registration (you must receive a letter confirming the registration to the event)


    Speakers

    Boris Tarovik

    RnD Engineer, Readdle

    Studied in Moscow Institute of Physics and Technology, department of molecular and Chemical Physics. Worked in Joint Institute for High Temperatures, where carried out molecular dynamic modeling of high-energy processes. Until today works in Readdle as RnD Engineer.

    Ivan Budnikov

    Machine Learning Engineer, Readdle

    Studied in Taras Shevchenko National University, Faculty of Physics
    He was engaged in research of nondifferentiable activation functions for neural networks and the detection of tables and formulas in PDF.
     

    Who will be interested in the workshop:

    Software requirements:


    Workshop Programme:

    Introduction

    • Data Science, Big Data, Machine Learning — what does it mean?
    • Machine Learning vs usual algorithms — what’s the difference?
    • Types of ML — supervised, reinforced, unsupervised

    Knowledge you need to have to become Data Scientist

    • theory vs practice
    • useful courses, articles, topics

    Differences in a work of Data Scientist and Software Developer

    • what data scientist’s debug is
    • think more often about a code than writing it

    Data science in product company vs. freelance

    • product is always about a quality and customers
    • data science is not only neural networks

    Lifecycle of ML solution development (Ivan)

    • Data Mining. Importance of good data. Data sources, data markup.
    • Cleaning data. Data augmentation. Training/Validation/Test split.
    • Using the ML-algorithm.
    • Result metrics — training, validation and test errors
    • Underfitting and overfitting — what is it and how to deal with.
    • Final evaluation. Precision, recall, F1-score.
    • Network optimisation for release.
    • Release. Brief review of future algorithm improvement: centralised after-training, decentralised after-training, combined.

    Review of simplest ML algorithms

    • K-means
    • PCA
    • LDA
    • Linear regression
    • Neural networks. What is it, where did it come from. Block notation. Some further improvements
    • convolutional nets
    • recurrent nets
    • LSTM

    Practical part 1: creating simplest ML solution for house price estimating, using sklearn/numpy

    Working examples of neural networks solutions in production

    • Readdle
    • Prisma
    • Google

    Practical part 2: creating neural network to solve computer vision problem, using tensorflow

    Sometimes things go wrong

    • lessons we’ve learned
    • practical recommendation

    Participation in the workshop is free, fill out the form and receive a confirmation of registration before June 18
    Date: June 23 (Saturday) 2018, 10:00 – 19:00
    Venue: Creative Quarter, Gulliver, Tower B, 12 floor, 1A Sportyvna Square, Kyiv
    Number of places is limited/30 participants. The registration will be closed on June 17 at 23:59.