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Onur – Python, Machine learning, Deep Learning, experts in Lemon.io

Onur

From Turkey (GMT+3)

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Machine Learning EngineerSenior
Data Scientist
Hire developer
5 years of commercial experience
AI
Climate tech
Computer science
Customer support
E-commerce
Environmental studies
Machine learning
Marketing
AI software
Geospatial software
GIS software
Identity verification software
SaaS
Video streaming software
Lemon.io stats
2
projects done
420
hours worked
Open
to new offers

Onur – Python, Machine learning, Deep Learning

Enthusiastic and inquisitive Data Scientist and Machine Learning Engineer with a central area of expertise in Computer Vision. One of his main passions is work, and there is no more enthusiastic person than him when it comes to discussing and presenting projects. Onur appreciates being of benefit to the world and never misses a chance to discover new things and challenge himself. He is motivated to work in an international environment and explore the microcosm of startups. All in all, Onur will definitely make a valuable contribution to your project.

Main technologies
Python
4 years
Machine learning
4 years
Deep Learning
4 years
OpenCV
3 years
PyTorch
4 years
Tensorflow
3 years
Additional skills
Git
NumPy
Postman
JSON
Linux
Keras
Scikit-learn
Pandas
Docker
AWS
AWS Lambda
Amazon S3
AWS CodeBuild
AWS SageMaker
Rewards and achievements
Client's favourite
Open source enthusiast
Ready to start
ASAP
Direct hire
Potentially possible

Experience Highlights

Senior Machine Learning Engineer (Computer Vision)
Jan 2023 - Apr 20232 months
Project Overview

Implementation of identity document detection within an e-commerce application, aimed at verifying the legality of customers to purchase specific products.

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Responsibilities:
  • Implemented a low-power deep learning model.
  • Fine-tuned algorithm parameters for precise detection of specific documents.
  • Corrected the viewing angle of ID documents for optimal Optical Character Recognition.
  • Successfully detected and classified passports, work permits, and driver's licenses, considering both country-wise and state-wise variations (European Union and United States).
  • Published the model using Amazon Web Services.
Project Tech stack:
Python
PyTorch
AWS Lambda
AWS CodeBuild
Amazon S3
OpenCV
Machine Learning Engineer (Computer Vision)
Feb 2022 - May 20223 months
Project Overview

SaaS platform to analyze the liveness of a user to overcome spoofing attacks. Combining 3 different aspects of liveness properties to understand whether the user is a real person:

  1. head pose estimation directs the user to turn his/her head in different directions in a specific order, then detects his/her head movements;

  2. eye-blink detection to count how many times the user blinks his/her eyes in a given time range;

  3. hand landmark detection and classification to classify hand signs of users.

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Responsibilities:
  • Designed head pose model without using facial key points unlike traditional methods;
  • avoided facial key points to reduce calculation cost and design a compact mechanism;
  • implemented new feature(ratio) to calculate distances of vertical eye and horizontal eye;
  • used two-stage approach as detecting hand landmarks and classification of them;
  • trained SVM to classify different hand signs.
Project Tech stack:
Python
Dlib
PyTorch
OpenCV
Docker
Machine Learning Engineer
Nov 2021 - Jan 20222 months
Project Overview

This software helps to understand the current emotions of users besides providing age & gender info. Our customer can evaluate how well communication between its agent, and its customer is based on provided data that was extracted.

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Responsibilities:
  • Designed model to extract age & gender with multi-head architecture;
  • used the same detection model with the Face Recognition system;
  • built age & gender feature extractor to be in the same model to reduce the calculation cost;
  • refactored emotion estimation model to cover 7 different moods of a person;
  • developed algorithms on ResNet architecture to overcome the overfitting issues.
Project Tech stack:
Python
Tensorflow
PyTorch
OpenCV
Docker
NumPy
Machine Learning Engineer
Jun 2021 - Sep 20213 months
Project Overview

End-to-end face recognition system which detects face and extracts specific features of individuals using deep learning methods. It ensures the detection of complex face samples that contain different scales, poses, occlusion, and blur. Then, it finds distinguishable characteristics of the detected face. Extracted features are used to calculate similarities between each other.

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Responsibilities:
  • Designed detection algorithm to be robust to hard samples;
  • made extra five-face landmark annotation to detect blurry faces;
  • used light-weight Mobile net architecture for detection to optimize memory allocation;
  • refactored loss function of recognition model to enhance separation of features from each other;
  • redesigned backend architecture for using deep learning models on either GPU or CPU.
Project Tech stack:
Python
OpenCV
PyTorch
Docker
Machine Learning Engineer (Computer Vision)
Apr 2021 - Jul 20213 months
Project Overview

Implementation of action recognition module with the help of deep learning to classify different motions in a compressed video. The developed module can analyze human-human, and human-object interactions to define actions.

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Responsibilities:
  • Compressed raw videos with MPEG-4 compression method;
  • extracted compressed components of a video to work on them
  • used these components (namely I-frame, motion vectors and residuals) to understand motion in a video;
  • implemented different deep learning models to work with three compressed video components;
  • developed an end-to-end module to classify more than a hundred action classes.
Project Tech stack:
Python
PyTorch
OpenCV
MPEG-4
Machine Learning Engineer
Mar 2018 - Jun 20183 months
Project Overview

Application of U-shaped deep learning method to segmental aerial images taken by UAV. It helps to analyze the partition of the earth's surface for different purposes such as monitoring green areas, changes in place of buildings, etc.

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Responsibilities:
  • Annotated raw images to be ground truth for training and test purposes;
  • designed encoder-decoder architecture for deep learning model;
  • implemented encoder network as VGG16, which is in charge of producing feature maps;
  • used upsampling-convolution operations to make a decoder network that maps low-resolution image to the intended segmented image;
  • made a deep learning model that segments images under different classes such as cars, buildings, roads, rails, etc.
Project Tech stack:
Python
PyTorch
OpenCV

Education

2018
Electronics and Communication Engineering
Bachelor's Degree
2022
Computer Science
Master's Degree

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