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gruzdev-as/README.md

Gruzdev Aleksandr - an Aircraft Engineer, a University Teacher, a Machine Learning Engineer

Fields of Interests: Classical Machine Learning and Deep Learning especially in the CV domain. Data Analysis and its practical application for decision making. Reinforcement Learning for complicated technical systems.

Open for the colloboration

Pinned Projects:

Mediawise Creative Filter. Digital Breakthrought 2024. (3-rd Place)

As part of a hackathon with the GNU MISIS team, a system was developed that predicts which segment (Segment_num) a given creative (Advertisement ID) belongs to.

I was involved in classifying video segments by their embeddings obtained using the XCLIP network, as well as developing theories and concepts for the generative neural network LLaVa. Presented the solution to the case holder at pitching.

IT Purple Hack: Churn Prediction case from SBER (4-th Place)

As part of the hackathon with the "MISIS and Mr. Smith" team was presenting the idea of a project to use models of varying sensitivity to be able to flexibly adjust the ratio of expended resources and potentially retained clients.

I built a CatBoostClassifier model, the hyperparameters of which were selected using the Optuna library, thanks to which it was possible to achieve the value of the ROC_AUC metric = 0.77+, which allowed us to take 5-th place on the public and 8-th place on the private leaderboard according to the results of the hackathon.

Dice Value Recognition: the Computer Vision Project (CV pet-project)

A set of Jupyter notebooks for training various computer vision models to classify and recognize dice of different configurations and face values. Full support of the project was carried out, starting from setting the technical specifications, to comparing the results obtained from different models, validating the results and drawing conclusions.

Digital Breakthrough 2022: Tula

My attempt to solve the issue within the framework of the Digital Breakthrough 2022 championship (Tula) - Creation of a model for predicting population diseases. During the work, data analysis, preprocessing and cleaning were carried out. Cross-validation has been implemented while preserving the time structure. The use of gradient boosting was considered as a basis for the solution; however, this did not give good results due to the peculiarities of the model’s operation and its inadequacy for solving problems of this class. Despite this, the work presents a basic pipeline for solving the problem.

Pinned

  1. Tula_Digital_Breakthrough Tula_Digital_Breakthrough Public

    My attempt to solve the issue within the framework of the Digital Breakthrough 2022 championship (Tula) - Creation of a model for predicting population diseases

    Jupyter Notebook

  2. Dice_recognition.The-computer-vision-project Dice_recognition.The-computer-vision-project Public

    A set of Jupyter notebooks for training various computer vision models for classifying and recognizing dice of various configurations and numbers on their surface

    Jupyter Notebook 1

  3. IT-Purple-Hack IT-Purple-Hack Public

    MISIS and Mr. Smith team

    Jupyter Notebook 4 2

  4. mediawise-creative-filter mediawise-creative-filter Public

    Forked from l1ghtsource/mediawise-creative-filter

    A service that predicts which segment it belongs to based on advertising creative

    Jupyter Notebook