Chesnokov Yuriy's starred repositories
nlp_course
YSDA course in Natural Language Processing
Deep-Reinforcement-Learning-Hands-On
Hands-on Deep Reinforcement Learning, published by Packt
www.mlcompendium.com
The Machine Learning & Deep Learning Compendium was a list of references in my private & single document, which I curated in order to expand my knowledge, it is now an open knowledge-sharing project compiled using Gitbook.
Machine-Learning-for-Algorithmic-Trading-Second-Edition
Code and resources for Machine Learning for Algorithmic Trading, 2nd edition.
reverse-geocoder
A fast, offline reverse geocoder in Python
Node.js-Design-Patterns-Third-Edition
Node.js Design Patterns Third Edition, published by Packt
Hands-On-Machine-Learning-for-Algorithmic-Trading
Hands-On Machine Learning for Algorithmic Trading, published by Packt
Machine-Learning-for-Algorithmic-Trading-Second-Edition_Original
Machine Learning for Algorithmic Trading, Second Edition - published by Packt
Deep-Learning-with-Keras
Code repository for Deep Learning with Keras published by Packt
openvino-plugins-ai-audacity
A set of AI-enabled effects, generators, and analyzers for Audacity®.
time-series-classification-and-clustering
Time series classification and clustering code written in Python.
InceptionTime
InceptionTime: Finding AlexNet for Time Series Classification
Interpretable-Machine-Learning-with-Python-2E
Interpretable ML with Python, 2E - published by Packt
ECG-MoCo-Classfication
Practical cardiac events intelligent diagnostic algorithm for wearable 12-lead ECG via self-supervised learning on large-scale dataset
www.opscompendium.com
The Ops Compendium is a resource list for dataops, mlops, devops, etc, which I'm actively curating in order to expand my knowledge, it is now an open knowledge-sharing project compiled using Gitbook.
Machine-Learning-Obesity-Classification
Classification on levels of obesity - data cleaning, exploratory analysis, preprocessing with pipeline, model selection and classification report.
epss-client
a Python client to query the FIRST EPSS API
BodyWeight_Imputation_Validation_Variability
Script associated with the publication: https://mhealth.jmir.org/preprint/17977. It describes how to deal with missing data, and the impact of missingness on linear and non-linear calculation of body weight variability, which may be an important marker in health epidemiology.