Kristjan Kongas

Machine Learning Engineer at Veeve Inc.
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Location
Tallinn, Harjumaa, Estonia, EE

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Credentials

  • Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
    Coursera
    Feb, 2018
    - Sep, 2024
  • Convolutional Neural Networks
    Coursera
    Jan, 2018
    - Sep, 2024
  • Neural Networks and Deep Learning
    Coursera
    Jan, 2018
    - Sep, 2024
  • Structuring Machine Learning Projects
    Coursera
    Dec, 2017
    - Sep, 2024

Experience

    • United States
    • Software Development
    • 1 - 100 Employee
    • Machine Learning Engineer
      • Jan 2023 - Present
    • Estonia
    • Computer Hardware Manufacturing
    • 1 - 100 Employee
    • Co-Founder
      • Feb 2022 - Jan 2023

      Built a prototype of a smart shopping cart, with automatic product recognition from cameras: * Built a large portion of the algorithmic and device handling parts of the software, such as: product recognition, 3d localization of the product using multiple cameras, handling cameras, weight sensors, and a dedicated QR code scanner from Linux/Python. * Designed and 3d printed plastic parts of the cart, and built the electronics Justacart scored Top 9 in Ajujaht 2022 and made it to Y Combinator W23 batch interview. Show less

    • Estonia
    • IT Services and IT Consulting
    • 1 - 100 Employee
    • Deep Learning Engineer
      • Mar 2017 - Apr 2022

      Developed, maintained and deployed deep learning based computer vision projects for timber pile images. Major successful projects: * Detecting log ends from images of timber piles. Decreased detection error rate 3x and detection latency by 6x. The resulting multi-stage algorithm consists of several different neural networks and custom filters. * Identical pile search algorithm: search for pictures made of the same pile. This is quite hard, as 1) pictures often look very similar, but are in fact of a different pile, 2) lighting/view angle/weather (eg. snow) can differ a lot between pictures, 3) an overlap might be partial (eg. some of the pile has been removed), 4) false positive rate must be very low to make the system usable * Detecting truck loads from pictures made from side views of trucks * Improved stitching of several parallel images of the same pile into one image Show less

Education

  • Tallinn Secondary Science School
    2013 - 2016

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