Venelin Valkov
Machine Learning Engineer at Dext
Bulgaria
Summary
🤩
RockstarVenelin Valkov is a Machine Learning Engineer with 11 years of experience building production ML systems and startups from Bulgaria, currently applying his expertise at Dext. He combines a strong engineering background—starting as a Java developer and later contributing machine learning expertise at Skyscanner—with hands-on model development and deployment. As co-founder of myPoli, he brings product-minded engineering and an entrepreneurial approach to solving real-world problems. Venelin is an active educator and contributor to ML learning resources, having developed and refined PyTorch Jupyter tutorials that cover topics from object detection to time-series anomaly detection. He blends practical deep learning know-how with production engineering rigor, often focusing on reproducible, notebook-driven workflows that accelerate team onboarding and experimentation.
11 years of coding experience
6 years of employment as a software developer
Stackoverflow
91reputation
6kreached
2answers
3questions
Github Skills (15)
user-manual10
pytorch10
jupyter-notebook10
machine-learning10
deep-learning10
numpy9
anomaly-detection8
code-coverage6
kotlin6
realm6
android-studio6
android-widget6
firebase6
android6
multithreading6
Programming languages (9)
Github contributions (5)
curiousily/Getting-Things-Done-with-Pytorch
Jan 2020 - Apr 2021
Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch. Topics: Face detection with Detectron 2, Time Series anomaly detection with LSTM Autoencoders, Object Detection with YOLO v5, Build your first Neural Network, Time Series forecasting for Coronavirus daily cases, Sentiment Analysis with BER
Role in this project:
Data Scientist
Data ScientistContributions:75 commits, 69 pushes, 1 branch in 1 year 3 months
Contributions summary:Venelin's commits focus on developing and modifying Jupyter Notebook tutorials for solving machine learning and deep learning problems with PyTorch. The primary area of focus is using PyTorch for various machine learning tasks, as indicated by the addition of notebooks on getting started with PyTorch, building a neural network, and anomaly detection. The changes primarily involve adding and updating notebook content, specifically code and markdown cells, to demonstrate concepts and provide guidance. The commits demonstrate a focus on using PyTorch and related libraries (NumPy).
forecastingcasesbertautoencodersjupyter-notebook
curiousily/dissertation
Mar 2017 - Mar 2018
Contributions:44 commits, 41 pushes, 1 branch in 1 year
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