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
292 878
Learning Perceptually-Aligned Representations via Adversarial Robustness
Article: https://arxiv.org/abs/1906.00945
Github: https://github.com/MadryLab/robust_representations
292 878
How to Perform Face Detection with Deep Learning in Keras
https://machinelearningmastery.com/how-to-perform-face-detection-with-classical-and-deep-learning-methods-in-python-with-keras/
292 878
A Gentle Introduction to Deep Learning for Face Recognition
https://machinelearningmastery.com/introduction-to-deep-learning-for-face-recognition/
292 878
Multi-Sample Dropout for Accelerated Training and Better Generalization
Link: https://arxiv.org/abs/1905.09788
292 878
EfficientNets
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
link: https://arxiv.org/abs/1905.11946.
292 878
How to Train an Object Detection Model to Find Kangaroos in Photographs (R-CNN with Keras)
https://machinelearningmastery.com/how-to-train-an-object-detection-model-with-keras/
292 878
SimpleSelfAttention
The purpose of this repository is two-fold:
-demonstrate improvements brought by the use of a self-attention layer in an image -classification model.
introduce a new layer which I call SimpleSelfAttention
https://github.com/sdoria/SimpleSelfAttention
292 878
AlphaFold: Использование ИИ для научных открытий
https://habr.com/ru/company/otus/blog/453848/
292 878
Augmented Neural ODEs
Github: https://github.com/EmilienDupont/augmented-neural-odes
Article: https://arxiv.org/abs/1904.01681
292 878
Unsupervised Learning with Graph Neural Networks
video: http://www.ipam.ucla.edu/programs/workshops/workshop-iv-deep-geometric-learning-of-big-data-and-applications/?tab=schedule
guide: http://helper.ipam.ucla.edu/publications/glws4/glws4_15546.pdf
292 878
How to Perform Object Detection With YOLOv3 in Keras
https://machinelearningmastery.com/how-to-perform-object-detection-with-yolov3-in-keras/
292 878
How degenerate is the parametrization of neural networks with the ReLU activation function?
https://arxiv.org/abs/1905.09803
292 878
illustrated Artificial Intelligence cheatsheets covering the content of the CS 221 class
Link: https://stanford.edu/~shervine/teaching/cs-221/
Reflex-based models with Machine Learning: https://stanford.edu/~shervine/teaching/cs-221/cheatsheet-reflex-models
292 878
COBRA: Data-Efficient Model-Based RL through Unsupervised Object Discovery and Curiosity-Driven Exploration
https://arxiv.org/abs/1905.09275
292 878
Torchvision 0.3: segmentation, detection models, new datasets
https://pytorch.org/blog/torchvision03/
