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Machinelearning

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

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📈 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 292 964 subscribers, ranking 328 in the Technologies & Applications category and 1 278 in the Russia region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 292 964 subscribers.

According to the latest data from 06 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -6 314 over the last 30 days and by -187 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 7.37%. Within the first 24 hours after publication, content typically collects 5.45% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 21 579 views. Within the first day, a publication typically gains 15 979 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 159.
  • 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 07 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.

292 964
Subscribers
-18724 hours
-1 3257 days
-6 31430 days
Posts Archive
Machine Learning Free Course with TensorFlow APIs by Google https://developers.google.com/machine-learning/crash-course/

We Can All Become Video Game Characters With This AI Video: https://www.youtube.com/watch?v=Y73iUAh56iI Paper: https://arxiv.org/abs/1904.08379

Создай первую модель машинного обучения за 3 дня! С 24 по 26 июня в 11:00 пройдет бесплатный интенсив по Data Science. Ссылка для регистрации 🔜 https://clc.to/isp30Q 🤖 С нуля создадим модель машинного обучения на Python и научим ее предсказывать курс доллара. 🎁 Участники с лучшими работами получат по 30 000 рублей. Будущее — за искусственным интеллектом!

Stand-Alone Self-Attention in Vision Models Paper: https://arxiv.org/abs/1906.05909

How to Implement GAN Hacks to Train Stable Generative Adversarial Networks https://machinelearningmastery.com/how-to-code-generative-adversarial-network-hacks/

Artificial Intelligence In Healthcare | Examples Of AI In Healthcare https://www.youtube.com/watch?v=j6EB9HO6acE

A collection of various deep learning architectures, models, and tips https://github.com/rasbt/deeplearning-models

Bayesian Deep Learning Benchmarks https://github.com/OATML/bdl-benchmarks

Weight Agnostic Neural Networks https://weightagnostic.github.io/

Robustness beyond Security: Computer Vision Applications http://gradientscience.org/robust_apps/

A Gentle Introduction to Generative Adversarial Networks (GANs) https://machinelearningmastery.com/what-are-generative-adversarial-networks-gans/

Как мы создали систему оповещения о ядерной угрозе https://habr.com/ru/post/452356/

Language, trees, and geometry in neural networks https://pair-code.github.io/interpretability/bert-tree/

18 Impressive Applications of Generative Adversarial Networks (GANs) A Generative Adversarial Network, or GAN, is a type of neural network architecture for generative modeling