现已上线！2025 年 Telegram 研究 — 年度关键洞察 
Machinelearning
前往频道在 Telegram
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
显示更多📈 Telegram 频道 Machinelearning 的分析概览
频道 Machinelearning (@ai_machinelearning_big_data) 俄语 语言赛道中的 是活跃参与者。目前社区聚集了 292 839 名订阅者，在 技术与应用 类别中位列第 328，并在 俄罗斯 地区排名第 1 282 位。
📊 受众指标与增长动态
自 невідомо 创建以来，项目保持高速增长，吸引了 292 839 名订阅者。
根据 06 七月, 2026 的最新数据，频道保持稳定运转。过去 30 天订阅人数变化为 -6 314，过去 24 小时变化为 -187，整体触达仍然可观。
- 认证状态： 未认证
- 互动率 (ER)： 平均受众互动率为 7.37%。内容发布后 24 小时内通常能获得 5.45% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 21 579 次浏览，首日通常累积 15 979 次浏览。
- 互动与反馈： 受众积极参与，单帖平均反应数为 159。
- 主题关注点： 内容集中在 openai, claude, api, gemini, контекст 等核心主题上。
📝 描述与内容策略
作者将该频道定位为表达主观观点的平台：
“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”
凭借高频更新（最新数据采集于 07 七月, 2026），频道始终保持新鲜度与高覆盖。分析显示受众积极互动，使其成为 技术与应用 类别中的关键影响点。
292 839
订阅者
-18724 小时
-1 3257 天
-6 31430 天
帖子存档
292 782
Mini Course in Deep Learning with PyTorch for AIMS
https://github.com/Atcold/pytorch-Deep-Learning-Minicourse
292 782
How I used Python to analyze Game of Thrones
https://medium.freecodecamp.org/how-i-used-python-to-analyze-game-of-thrones-503a96028ce6
292 782
MorphNet: Towards Faster and Smaller Neural Networks
http://ai.googleblog.com/2019/04/morphnet-towards-faster-and-smaller.html
292 782
Take Your Best Selfie Automatically, with Photobooth on Pixel 3
http://ai.googleblog.com/2019/04/take-your-best-selfie-automatically.html
292 782
Week 8 (part c) CS294-158 Deep Unsupervised Learning (4/3/19) -- Ilya Sutskever
https://www.youtube.com/watch?v=X-B3nAN7YRM
292 782
Основы Natural Language Processing для текста
https://habr.com/ru/company/Voximplant/blog/446738/
292 782
How to Use Test-Time Augmentation to Improve Model Performance for Image Classification
https://machinelearningmastery.com/how-to-use-test-time-augmentation-to-improve-model-performance-for-image-classification/
292 782
Gentle Dive into Math Behind Convolutional Neural Networks
https://towardsdatascience.com/gentle-dive-into-math-behind-convolutional-neural-networks-79a07dd44cf9
292 782
How to Configure Image Data Augmentation When Training Deep Learning Neural Networks
https://machinelearningmastery.com/how-to-configure-image-data-augmentation-when-training-deep-learning-neural-networks/
292 782
Review: Residual Attention Network — Attention-Aware Features (Image Classification)
https://towardsdatascience.com/review-residual-attention-network-attention-aware-features-image-classification-7ae44c4f4b8
292 782
How to Load Large Datasets From Directories for Deep Learning with Keras
https://machinelearningmastery.com/how-to-load-large-datasets-from-directories-for-deep-learning-with-keras/
292 782
How to Get Started With Deep Learning for Computer Vision (7-Day Mini-Course)
https://machinelearningmastery.com/how-to-get-started-with-deep-learning-for-computer-vision-7-day-mini-course/
292 782
How to Load and Visualize Standard Computer Vision Datasets With Keras
https://machinelearningmastery.com/how-to-load-and-visualize-standard-computer-vision-datasets-with-keras/
292 782
Introduction to Tensorflow 2.0 | Tensorflow 2.0 Features and Changes
https://www.youtube.com/watch?v=3O-5DuqKaRo
