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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 292 747 subscribers, ranking 328 in the Technologies & Applications category and 1 291 in the Russia region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 292 747 subscribers.

According to the latest data from 07 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -6 317 over the last 30 days and by -209 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 7.45%. Within the first 24 hours after publication, content typically collects 5.46% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 21 817 views. Within the first day, a publication typically gains 15 977 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 160.
  • 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 08 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.

292 747
Subscribers
-20924 hours
-1 3687 days
-6 31730 days
Posts Archive
How to Improve Performance With Transfer Learning for Deep Learning Neural Networks https://machinelearningmastery.com/how-to-improve-performance-with-transfer-learning-for-deep-learning-neural-networks/

Intuitive Deep Learning Part 1a: Introduction to Neural Networks What is Deep Learning? A very gentle and intuitive introduction to Neural Networks and how they work! https://towardsdatascience.com/intuitive-deep-learning-part-1a-introduction-to-neural-networks-aaeb3a1500df

The Best of AI: New Articles Published This Month (January 2019) 10 data articles handpicked by the Sicara team, just for you https://blog.sicara.com/01-2019-best-ai-new-articles-this-month-8e2113fbd17b

The Quick Python Book 2018

Designing non-linear navigation for machine learning and topic modeling experiences https://uxdesign.cc/designing-non-linear-navigation-for-machine-learning-and-topic-modeling-experiences-4ee969875ebe

Improving Evolutionary Strategies with Generative Neural Networks https://arxiv.org/abs/1901.11271

Искусственные нейронные сети выращивают навигационные клетки как в мозге https://habr.com/ru/post/438526/

Predicting Kickstarter Campaign Success with Gradient Boosted Decision Trees: A Machine Learning Classification Problem https://medium.com/@rileypredum/predicting-kickstarter-campaign-success-with-gradient-boosted-decision-trees-a-machine-learning-23077436c5f7

Browse state-of-the-art 509 leaderboards • 963 tasks • 700 datasets • 8598 papers with code https://paperswithcode.com/sota

Interactive Controls in Jupyter Notebooks How to use interactive IPython widgets to enhance data exploration and analysis https://towardsdatascience.com/interactive-controls-for-jupyter-notebooks-f5c94829aee6

Machine Learning with TensorFlow

How to build an image classifier with greater than 97% accuracy https://medium.freecodecamp.org/how-to-build-the-best-image-classifier-3c72010b3d55

Google Researchers Have a New Alternative to Traditional Neural Networks Say hello to the capsule network. https://www.technologyreview.com/the-download/609297/google-researchers-have-a-new-alternative-to-traditional-neural-networks/