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Danil

  • Machine Learning
  • Python

Danil is a data scientist with 3 years of experience in machine learning.

Danil and more developers are now available for hire.

Hire Danil

Skills and Qualifications

Languages

  • Python

Frameworks

  • PyTorch
  • PyQt

Libraries/APIs

  • Tensorflow
  • Tensorflow-Serving
  • Keras
  • librosa

Tools

  • YOLOv3

Projects

BusFactor

Data Scientist

The main task of the project was to monitor public transport (buses, trams, etc.) drivers and record the cases of mobile phone usage while driving. The system was also required to check whether the camera in the driver's cabin is rotated correctly and monitor it to ensure that it is working all the time. He also organized the deployment of the final models to the client's machine.


  • Python
  • Keras
  • Tensorflow
  • Tensorflow-Serving
  • YOLOv3

Recognition of goods on the shelves

Data Scientist

A solution was needed for merchandisers who compare the real arrangement of goods (realograms) with a plan (planogram). OCR and one-shot learning were used to search the product database using triplet loss network training. The generation of images in Blender was required, as was the development of tools marking real goods. Danil experimented with various detection and classification approaches, taught OCR models, developed image generation using Blender, and ported and optimized models for Android devices.


  • Python
  • Keras
  • Tensorflow
  • Tensorflow-Serving
  • YOLOv3
  • one-shot learning

Vehicle detection and license plate recognition

Data Scientist

A system to solve the problem of finding traffic violations. For the solution, it was necessary to detect cars, compare and identify cars from different camera angles, find violations, and recognize license plates. The project required different OCR approaches for license plate recognition, etc.


  • PyTorch

Video Podcasts

Data Scientist

An application for breaking text into paragraphs for a podcast startup. Danil undertook the task of creating a system to automatically break text up into semantic sections. Various NLP approaches were used, as were neural networks - based on transformers such as BERT.


  • Android
  • Python
  • PyTorch
  • Docker
  • MLFlow
  • DVC.