Tuguldur Erdene
Chief Executive Officer at Young Shapers Mongolia
Ulaanbaatar, Mongolia
Summary
🤩
Rockstar🎓
Top SchoolTuguldur Erdene is an AI-focused technology leader and CEO based in Ulaanbaatar with 11 years of experience bridging data science, electrical engineering, and startup leadership. He founded and now leads Young Shapers Mongolia while drawing on hands-on ML engineering work showcased in a popular dl-colab-notebooks repo that demonstrates diverse deep learning models from Mask R-CNN to Tacotron2. A Kaggle Competitions Grandmaster educated at the Technical University of Berlin and currently studying at Rice University, he combines competitive modeling skill with practical deployments and experimentation on Google Colab. His background includes internships in data science and electrical engineering, giving him a rare mix of algorithmic depth and hardware-aware systems thinking. Tuguldur is comfortable moving between research-grade models and productized prototypes, often adapting state-of-the-art architectures for real-world vision and speech tasks. Colleagues describe him as entrepreneurial and technically curious, consistently turning competition-grade solutions into accessible demos and tools.
11 years of coding experience
Rice UniversityHigh School Diploma, High School Diploma at Olonlog Tuv
English, Spanish
Github Skills (13)
computer-vision10
machine-learning10
deep-learning10
integrate10
integrations10
data-integration10
pytorch9
speech-recognition8
faster-rcnn8
mask-rcnn8
yolov48
tensorflow7
taco7
Programming languages (7)
Github contributions (5)
tugstugi/dl-colab-notebooks
Mar 2019 - Mar 2022
Try out deep learning models online on Google Colab
Role in this project:
ML Engineer
ML EngineerContributions:121 commits, 6 PRs, 114 pushes in 3 years
Contributions summary:Tuguldur's contributions focus on integrating and testing various deep learning models for computer vision tasks within the "dl-colab-notebooks" repository. The commits demonstrate the user's involvement with several different models including: Tacotron2 and Waveglow for text-to-speech, DeepSORT for pedestrian tracking with YOLOv3, Mask R-CNN, 3D car detection with CenterNet, a speech recognition model based on an OCR network (CRNN), and model-specific code adjustments. The user appears to be focused on demonstrating and testing diverse machine learning models.
pytorchpythondeep-learninggoogle-colabgoogle-colaboratory
tugstugi/mongolian-speech-recognition
Sep 2018 - Sep 2020
Mongolian speech recognition with PyTorch
Contributions:132 commits, 92 pushes, 10 branches in 2 years 1 month
pytorchrecognitionspeech-recognitionmongolianspeech
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