Mikhail's repositories
Time-Series-Hybrid-Autoencoder
Remaining Useful Life estimation and sensor data generation by VAE and diffusion model on C-MAPSS dataset.
GMVAE-pytorch
Pytorch implementation of Gaussian Mixture Variational Autoencoder GMVAE
twitter-parser
Twitter Data Scraper: A collection of Python scripts for scraping and processing Twitter data using tweepy library. Includes tweets machine translation to english.
AB-test-simulator
This Streamlit application simulates A/B tests, providing a platform to evaluate the performance of different statistical tests based on data distribution.
BertAttentionViz
BERT Attention Visualization is a web application powered by Streamlit, offering intuitive visualization of attention weights generated by BERT-based models.
RecSys-Core-Algorithms
A comprehensive repository implementing various recommender system algorithms, including Naive Methods, ALS, NCF, NeuMF, DSSM, and more. Explore different recommendation techniques and their implementations for personalized user experiences.
annotated_deep_learning_paper_implementations
🧑🏫 60 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
Data_Analysis_in_Economics_and_Finance
The materials for the course 'Data Analysis in Economics and Finance', 2020/2021, NRU HSE.
deep_vision_and_graphics
Course about deep learning for computer vision and graphics co-developed by YSDA and Skoltech.
hypothesis-testing
Collection of Jupyter Notebooks covering fundamental concepts such as hypothesis testing, statistical power, and various tests for one or two samples, as well as variance. Most functions and test are written from scratch for educational purposes.
insdout.github.io
Use this template if you need a quick developer / data science portfolio! Based on a Minimal Jekyll theme for GitHub Pages.
ML-Algorithms-From-Scratch
Implementations of main Machine Learning Agorithms from scratch: Gaussian Mixture Model, Gradient Boosting, Adam, RMSProp, PCA, QR, Eigendecomposition, Decision Trees etc.
ml_observability_course
Free Open-source ML observability course for data scientists and ML engineers. Learn how to monitor and debug your ML models in production.
nlp_course
YSDA course in Natural Language Processing
Practical_RL
A course in reinforcement learning in the wild
Practical_DL
DL course co-developed by YSDA, HSE and Skoltech