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  • GANs for tabular data

    We well know GANs for success in the realistic image generation. However, they can be applied in tabular data generation. We will review and examine some recent papers about tabular GANs in action.

  • Guide how to learn and master computer vision in 2020

    This post will focus on resources, which I believe will boost your knowledge in computer vision the most and mainly based on my own experience.

  • Severstal Steel Defect Detection Challenge on Kaggle

    Top 2% (31/2431) solution write-up. Steel is one of the most important building materials of modern times. Steel buildings are resistant to natural and man-made wear which has made the material ubiquitous around the world. To help make production of steel more efficient, this competition will help identify defects.

  • Talk: Banking models interpretation

    Talk was given at AI Journey Conference in Moscow. Conference with leading international and Russian experts in AI and data analysis, top companies in the development and application of AI in business

  • Kaggle APTOS 2019 Blindness Detection Challenge

    Top 3% (76/2943) solution write-up for the Kaggle APTOS 2019 Blindness Detection. Imagine being able to detect blindness before it happened. Millions of people suffer from diabetic retinopathy, the leading cause of blindness among working aged adults

  • Road detection using segmentation models and albumentations libraries on Keras

    In this article, I will show how to write own data generator and how to use albumentations as augmentation library. Along with segmentation_models library, which provides dozens of pretrained heads to Unet and other unet-like architectures. For the full code go to Github. Link to dataset.

  • Poster: Automatic salt deposits segmentation: A deep learning approach

    Being honored to present a poster about image segmentation at the last international summit, Machines Can See 2019 , Moscow, Russia #deeplearning #cv #poster

  • Anomaly detection in time series with Prophet library

    Anomaly detection problem for time series can be formulated as finding outlier data points relative to some standard or usual signal. While there are plenty of anomaly types, we’ll focus only on the most important ones from a business perspective, such as unexpected spikes, drops, trend changes and level shifts by using Prophet library.

  • Kaggle Salt Identification Challenge or how to segment images

    Top 1% (28/3229) solution write-up, based on a single 5-Unet like model with hflip TTA (test time augmentation) and few other tricks.*