Machinelearning

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Погружаемся в машинное обучение и Data Science Показываем как запускать любые LLm на пальцах. По всем вопросам - @haarrp @itchannels_telegram -🔥best channels Реестр РКН: clck.ru/3Fmqri

Network:Machinelearning Russia1 292 Technologies & Applications328...

📈 Analytical overview of Telegram channel Machinelearning

Channel Machinelearning (@ai_machinelearning_big_data) in the Russian language segment is an active participant. Currently, the community unites 292 329 subscribers, ranking 328 in the Technologies & Applications category and 1 292 in the Russia region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 292 329 subscribers.

According to the latest data from 09 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -6 200 over the last 30 days and by -193 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 7.33%. Within the first 24 hours after publication, content typically collects 5.53% reactions from the total number of subscribers.
Post reach: On average, each post receives 21 439 views. Within the first day, a publication typically gains 16 173 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 154.
Thematic interests: Content is focused on key topics such as openai, claude, api, gemini, контекст.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“Погружаемся в машинное обучение и Data Science Показываем как запускать любые LLm на пальцах. По всем вопросам - @haarrp @itchannels_telegram -🔥best channels Реестр РКН: clck.ru/3Fmqri”

Thanks to the high frequency of updates (latest data received on 10 July, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.

292 329

Subscribers

-19324 hours

-1 2987 days

-6 20030 days

21 439

Post views

~ 16 17324 hours

~ 17 79848 hours

7.33%

Engagement rate

~ 6

Posts per day

Ads index

beta

Posts Archive

292 341

Introducing the Unrestricted Adversarial Examples Challenge https://ai.googleblog.com/2018/09/introducing-unrestricted-adversarial.html

292 341

GMM-UBM и алгоритма MAP adaptation https://habr.com/post/420515/

292 341

Highlights from the European Conference on Computer Vision 2018 https://blog.alookanalytics.com/2018/09/12/highlights-from-the-european-conference-on-computer-vision-2018/

292 341

Healthcare Dataset with #PySpark Back to some #BigData stuff: article about quick-and-dirty development of prediction algorithm using #Spark engine. Link: https://towardsdatascience.com/healthcare-dataset-with-spark-6bf48019892b

292 341

Simple introduction to the ALU for neural networks: explanation, physical meaning and implementation https://habr.com/post/422777/

292 341

Lite Intro into Reinforcement Learning https://towardsdatascience.com/lite-intro-into-reinforcement-learning-857ca5c924d9?gi=692d05ca6c3d

292 341

Conceptual Captions: A New Dataset and Challenge for Image Captioning https://ai.googleblog.com/2018/09/conceptual-captions-new-dataset-and.html

292 341

Analysis of the tonality of texts using convolutional neural networks https://habr.com/company/mailru/blog/417767/

292 341

Google launches new search engine to help scientists find the datasets they need https://www.theverge.com/2018/9/5/17822562/google-dataset-search-service-scholar-scientific-journal-open-data-access

292 341

Intel AI Interview Questions — Acing the AI Interview https://medium.com/acing-ai/intel-ai-interview-questions-acing-the-ai-interview-d4a62dbb593c

292 341

11-485/785 Introduction to Deep Learning Fall 2018 http://deeplearning.cs.cmu.edu/

292 341

Глагол «грокать», придуманный фантастом Робертом Хайнлайном более полувека назад, означает знать и понимать что‑либо так, что это стало частью тебя, частью твоей жизни. Разница между «грокать» и просто «знать» — как между владением родным языком и хорошо изученным иностранным. Рекомендуем отличный канал — @Groks о технологиях и аналитике. Там вы найдете отчёты, данные, графики, новости, подборки статей на русском и английском, и авторское описание последних событий по заданной теме. В общем, @Groks — канал для диджитал гроккеров, для тех, кто не смотрит на мир диджитал со стороны, а живёт в нём.

292 341

How and When to Use a Calibrated Classification Model with scikit-learn https://machinelearningmastery.com/calibrated-classification-model-in-scikit-learn/

292 341

Neural nets in Android, Google ML Kit https://habr.com/post/422041/

292 341

Introducing a New Framework for Flexible and Reproducible Reinforcement Learning Research https://ai.googleblog.com/2018/08/introducing-new-framework-for-flexible.html

292 341

A Style-Aware Content Loss for Real-time HD Style Transfer Source code for the ECCV18 paper: https://github.com/CompVis/adaptive-style-transfer

292 341

What are the experts in data analysis really doing? Conclusions from 35 interviews https://habr.com/company/wirex/blog/421845/

292 341

How and When to Use ROC Curves and Precision-Recall Curves for Classification in Python https://machinelearningmastery.com/roc-curves-and-precision-recall-curves-for-classification-in-python/

292 341

The neural network was trained to recognize depression by an arbitrary human speech without context https://habr.com/post/421775/

292 341

HyperparameterHunter Easy hyperparameter optimization and automatic result saving across machine learning algorithms and libraries https://github.com/HunterMcGushion/hyperparameter_hunter