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 292 964 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 1 278 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 292 964 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。
292 964
订阅者
-18724 小时
-1 3257 天
-6 31430 天
帖子存档
292 918
A Gentle Introduction to Upsampling and Transpose Convolution Layers for GANs
Generative Adversarial Networks, or GANs, are an architecture for training generative models, such as deep convolutional neural networks for generating images
292 918
Machine Learning Free Course with TensorFlow APIs by Google
https://developers.google.com/machine-learning/crash-course/
292 918
Adapters: A Compact and Extensible Transfer Learning Method for NLP
https://medium.com/dair-ai/adapters-a-compact-and-extensible-transfer-learning-method-for-nlp-6d18c2399f62
292 918
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
292 918
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!
292 918
The 2019 IEEE Conference on Computer Vision and Pattern Recognition
Best Papers
https://syncedreview.com/2019/06/18/cvpr-2019-attracts-9k-attendees-best-papers-announced-imagenet-honoured-10-years-later/
292 918
How to Implement GAN Hacks to Train Stable Generative Adversarial Networks
https://machinelearningmastery.com/how-to-code-generative-adversarial-network-hacks/
292 918
Artificial Intelligence In Healthcare | Examples Of AI In Healthcare
https://www.youtube.com/watch?v=j6EB9HO6acE
292 918
Tips for Training Stable Generative Adversarial Networks
https://machinelearningmastery.com/how-to-train-stable-generative-adversarial-networks/
292 918
A collection of various deep learning architectures, models, and tips
https://github.com/rasbt/deeplearning-models
292 918
Robustness beyond Security: Computer Vision Applications
http://gradientscience.org/robust_apps/
292 918
A Gentle Introduction to Generative Adversarial Networks (GANs)
https://machinelearningmastery.com/what-are-generative-adversarial-networks-gans/
292 918
Language, trees, and geometry in neural networks
https://pair-code.github.io/interpretability/bert-tree/
292 918
Applying AutoML to Transformer Architectures
http://ai.googleblog.com/2019/06/applying-automl-to-transformer.html
292 918
18 Impressive Applications of Generative Adversarial Networks (GANs)
A Generative Adversarial Network, or GAN, is a type of neural network architecture for generative modeling
