Machine Learning Engineer
2021 - PRESENTG42- Delivered a Computer Vision project to automatically read ID documents scans.
- Developed Computer Vision software to detect road traffic violations.
- Delivered a PCR registration chatbot, the basis was used to create other services later on.
Technologies: PyTorch, Python 3, Solidity, You Only Look Once (YOLO), Game Theory, Computer Vision, Object Tracking, Chatbots, Rasa NLU, Moralis, Artificial Intelligence (AI), Ubuntu, Microsoft Excel, Jira, Agile Software Development, Machine Learning, Random Forests, Linear Regression, Logistic Regression, PyCharm, XGBoost, Scikit-learn, Sublime Text 3, Sublime Text, Video Processing, Image ProcessingComputer Vision Developer
2019 - 2020MindsLenses- Created a pipeline that processes everyday movies collected from RPI cameras. The processing includes several deep learning algorithms that detect human emotions in association with product interactions that are being handled in the store.
- Trained deep learning models for object detection and classification. Utilized state-of-the-art frameworks to produce the best quality predictions.
- Managed stable process of videos from raspberry pies to CV inference on the AWS cloud.
Technologies: Amazon Web Services (AWS), Deep Learning, Python, AWS, PyTorch, SQL, PostgreSQL, Machine LearningMachine Learning Engineer
2017 - 2019Sportradar US- Created a neural network for the NFL to generate drive outcome predictions to further use from the betting companies.
- Built NBA shot probability neural network models using Pytorch for fan experience enhancement.
- Identified NBA events on games using tracking features with XGBoost.
- Implemented an NBA data pipeline from different data sources.
- Created an NBA similarity model for draft players for fan enhanced experience.
Technologies: PythonData Scientist
2016 - 2017Alem Research- Scraped 10 million company front pages from a social network with Java.
- Created a sentiment analysis neural network model using TensorFlow to label various English texts (including articles, comments, and more) as positive, negative, or neutral.
Technologies: Java, Python
Uan Sholanbayev
Computer Vision Developer in Almaty, Kazakhstan
Member since October 11, 2019
After earning a degree in computer engineering from the University of California, San Diego, Uan has gained much in-depth and hands-on experience delivering services as a machine learning engineer. He is an expert in Python, deep learning, neural networks, and other AI algorithms. Uan is also an exemplary communicator and has worked in teams as well as individually.
Uan is now available for hire
Portfolio
- G42PyTorch, Python 3, Solidity, You Only Look Once (YOLO), Game Theory...
- MindsLensesAmazon Web Services (AWS), Deep Learning, Python, AWS, PyTorch, SQL...
- Sportradar USPython
Experience
- PostgreSQL 5 years
- MongoDB 5 years
- Git 5 years
- PyTorch 5 years
- Python 5 years
- Amazon EC2 (Amazon Elastic Compute Cloud) 5 years
- Computer Vision 5 years
- Deep Learning 5 years
Availability
Part-time
Preferred Environment
GitLab, PyCharm, Ubuntu, MacOS, Amazon EC2 (Amazon Elastic Compute Cloud), MongoDB, Python 3, PyTorch, You Only Look Once (YOLO)
The most amazing...
...project I've worked on was an NFL drive prediction system based on neural networks. The predictions are used by betting companies to come up with coefficients.
Employment
Experience
- Neural Network Distilling Knowledgehttps://github.com/usholanb/Experiments-with-Distilling-Knowledge.git
One of the hottest topics in the field of neural networks is knowledge distillation. Data scientists are curious about how compacted a neural net can become while still performing at a similar quality level.
The approach is to use the layer before the last (output layer) and use it as an output layer for a smaller neural net (with less hidden layers).
In this project, I used ten classes of ImageNet (around 1,000 pictures each) and trained two such neural nets. The results were the large model was 68% top 1. The smaller model (trained itself) was 56% top 1, and the distilled model was 56.5% top 1. - NFL Drive Outcome Prediction
I implemented a neural network model for NFL drive outcome predictions. The framework is Pytorch and the predictions are used by betting companies for coefficients.
Skills
Languages
Python, Java, SQL, C++, C, Scala, Python 3, SolidityLibraries/APIs
OpenCV, PyTorch, Scikit-learn, XGBoost, Lasagne, TensorFlow, Rasa NLUTools
Git, Jira, Slack, SourceTree, Robomongo, PyCharm, Sublime Text 3, Microsoft Excel, MATLAB, GitLab, Sublime Text, You Only Look Once (YOLO)Paradigms
Object-oriented Programming (OOP), Agile Software DevelopmentPlatforms
Amazon EC2 (Amazon Elastic Compute Cloud), Ubuntu, MacOS, Amazon Web Services (AWS)Storage
Amazon S3 (AWS S3), MongoDB, PostgreSQL, ElasticsearchOther
Artificial Intelligence (AI), Convolutional Neural Networks, Image Processing, Machine Learning, Video Processing, Neural Networks, Statistical Modeling, Deep Learning, Random Forests, Linear Regression, Logistic Regression, Data Versioning, Tf-idf, K-means Clustering, K-nearest Neighbors, Computer Vision, Natural Language Processing (NLP), AWS, Game Theory, Object Tracking, Chatbots, Moralis
Education
- Bachelor of Science Degree in Computer Engineering2012 - 2016University of California, San Diego - San Diego, CA, USA
Certifications
- Machine LearningSEPTEMBER 2017 - PRESENTStanford University via Coursera
