Machinelearning
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
Show more📈 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 295 152 subscribers, ranking 332 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 295 152 subscribers.
According to the latest data from 25 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -6 406 over the last 30 days and by -274 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 7.97%. 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 23 518 views. Within the first day, a publication typically gains 16 322 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 183.
- 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 26 June, 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.
pip3 install hidiffusion
• page: https://hidiffusion.github.io
• paper: https://arxiv.org/abs/2311.17528
• code: https://github.com/megvii-research/HiDiffusion
•colab: https://colab.research.google.com/drive/1EiBn9lSnPZTU4cikRRaBBexs429M-qty?usp=sharing
@ai_machinelearning_big_datagit clone https://github.com/ShineChen1024/MagicClothing.git
▪Github
▪Paper
@ai_machinelearning_big_dataMACHINELEARNING
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