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
292 903
Deep Neural Networks from scratch in Python
https://towardsdatascience.com/deep-neural-networks-from-scratch-in-python-451f07999373
292 903
CVPR 2019
Papers: http://openaccess.thecvf.com/CVPR2019.py
Workshops: http://openaccess.thecvf.com/CVPR2019_workshops/menu.py
292 903
Disentangling Disentanglement in Variational Autoencoders
Article.: http://proceedings.mlr.press/v97/mathieu19a.html
292 903
Hierarchical Representation in Neural Language Models: Suppression and Recovery of Expectations
https://arxiv.org/abs/1906.04068
292 903
Серверы с GPU от 90 рублей в час! 🔥
Облачные вычисления на базе графических ускорителей Tesla V100 отлично подойдут для машинного обучения, анализа данных и высокопроизводительных вычислений. 👀
Подробности по ссылке: http://bit.ly/2KEZPcm
292 903
Deep Learning Frameworks 2019 | Which Deep Learning Framework To Use | Deep Learning
https://www.youtube.com/watch?v=6ryPbOfz03U
292 903
One-Shot Learning with Siamese Networks, Contrastive Loss, and Triplet Loss for Face Recognition
https://machinelearningmastery.com/one-shot-learning-with-siamese-networks-contrastive-and-triplet-loss-for-face-recognition/
292 903
DL-workshop-series
Github: https://github.com/Machine-Learning-Tokyo/DL-workshop-series
Link to the presentation: https://drive.google.com/open?id=1sXztx3E9M3G0BIRLh6sxaqVOEOdJVJTrzHOixA5b-rM
Videos: https://www.youtube.com/playlist?list=PLaPdEEY26UXxvlzz485w61W4LgO0lUZfg
292 903
Implementing a Simple Auto-Encoder in Tensorflow
https://medium.com/@barp.edoardo/implementing-a-simple-auto-encoder-in-tensorflow-1181751f202
292 903
How to Develop a Face Recognition System Using FaceNet in Keras
https://machinelearningmastery.com/how-to-develop-a-face-recognition-system-using-facenet-in-keras-and-an-svm-classifier/
292 903
Отличаем символы от мусора: как построить устойчивые нейросетевые модели в задачах OCR
https://habr.com/ru/company/abbyy/blog/449524/
292 903
Welcome to TensorWatch
TensorWatch is a debugging and visualization tool designed for deep learning and reinforcement learning from Microsoft Research. It works in Jupyter Notebook to show real-time visualizations of your machine learning training and perform several other key visualizations of your models and data.
https://github.com/microsoft/tensorwatch/
292 903
Free COURSE. CS Deep Reinforcement Learning UC Berkeley
Video Lectures: https://www.youtube.com/playlist?list=PLkFD6_40KJIxJM..
Lecture Material: http://rail.eecs.berkeley.edu/deeprlcourse/
292 903
Introducing TensorNetwork, an Open Source Library for Efficient Tensor Calculations
http://ai.googleblog.com/2019/06/introducing-tensornetwork-open-source.html
