deepmachinelearning.ru

What It Is

deepmachinelearning.ru is a set of Russian-language online learning materials focused on machine learning and deep learning. It was written by Viktor Vladimirovich Kitov, with content drawn from foundational topics the author taught at the Faculty of Computational Mathematics and Cybernetics at Moscow State University, as well as from the T-Bank undergraduate course for students at the Moscow Institute of Physics and Technology. The textbook has won an award in a RuCode Science and Technology Festival “AI for all” selection, and the underlying “Machine Learning Methods” course has also won an award in VK’s IT course competition.

Core Content and Format

The material is divided into two major parts. The first covers classical machine learning, including regression, classification, data preparation, model tuning, quality evaluation, ensembles, boosting, dimensionality reduction, clustering, and model interpretation. The second covers deep learning, including neural network training, regularization, convolutional networks, image processing, object detection, semantic/instance segmentation, word and paragraph embeddings, recurrent networks, Transformers, and graph data processing. The teaching format is primarily web-based text material, emphasizing simple examples, diagrams, mathematical formulas, and pseudocode to explain how algorithms work, along with explanations of terminology and notation.

Pricing and Certificates

The crawled text does not mention fees, subscriptions, paid courses, or payment methods, and the pages appear to be directly readable online. However, the materials are copyright protected, so specific usage rights should be checked on the site’s license page. The text also does not mention course completion certificates, professional credentials, or exam-based certification, so it should not be treated as an online course platform that provides certificates.

Pros and Cons

Its strengths are a complete structure and a clear theoretical orientation, making it suitable for learners who want to truly understand algorithmic principles rather than simply stack neural network layers. It also specifically serves Russian-speaking learners, with one of its goals being to reduce the information gap between English and Russian resources. The drawbacks are also clear: the teaching language is Russian, which creates a relatively high barrier for Chinese users; the crawled content does not show videos, graded assignments, community Q&A, or a learning progress system; and it mainly focuses on foundational topics rather than tracking the latest cutting-edge methods. The author also suggests using Papers With Code to follow frontier developments.

Who It’s For and Access from China

It is suitable for students with a background in higher mathematics who want to learn machine learning and deep learning systematically from scratch. It can also serve as a Russian-language reference manual for learners who already have some foundation. For users who mainly want certificates, hands-on project coaching, or Chinese-language explanations, it is not the best fit. Access from mainland China cannot be determined from the crawled text, so it is marked as unknown.