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 292 747 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 1 291 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 292 747 名订阅者。
根据 07 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 -6 317，过去 24 小时变化为 -209，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 7.45%。内容发布后 24 小时内通常能获得 5.46% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 21 817 次浏览，首日通常累积 15 977 次浏览。
- 互动与反馈： 受众积极参与，单帖平均反应数为 160。
- 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 08 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。
292 747
订阅者
-20924 小时
-1 3687 天
-6 31730 天
帖子存档
292 743
NLP Learning Series: Part 3 - Attention, CNN and what not for Text Classification
https://mlwhiz.com/blog/2019/03/09/deeplearning_architectures_text_classification/
292 743
“Deep learning” about transcription factor-DNA binding
Using a convolutional neural network to measure transcription factor-DNA binding
https://towardsdatascience.com/deep-learning-about-transcription-factor-dna-binding-1d9753eabcc2
292 743
Google Open-Sources GPipe Library for Training Large-Scale Neural Network Models
https://medium.com/syncedreview/google-open-sources-gpipe-library-for-training-large-scale-neural-network-models-8b8ef324382c
292 743
How to get started with Python for Deep Learning and Data Science
A step-by-step guide to setting up Python for a complete beginner
https://medium.freecodecamp.org/how-to-get-started-with-python-for-deep-learning-and-data-science-3bed07f91a08
292 743
6 AI Developments to Follow in 2019
https://medium.com/@jaxson/6-ai-developments-to-follow-in-2019-2642a446d0c1?source=topic_page---------1------------------1
292 743
How to Demonstrate Your Basic Skills with #DeepLearning
https://machinelearningmastery.com/how-to-demonstrate-basic-deep-learning-competence/
292 743
Exploring Neural Networks with Activation Atlases
http://ai.googleblog.com/2019/03/exploring-neural-networks.html
292 743
Announcement: TensorFlow 2.0 Has Arrived!
Few months ago I mentioned some of the exciting new features that will be included in TensorFlow 2.0.
https://towardsdatascience.com/announcement-tensorflow-2-0-has-arrived-ee59283fd83a
292 743
3 Levels of Deep Learning Competence
https://machinelearningmastery.com/deep-learning-competence/
292 743
Build your own Robust Deep Learning Environment in Minutes
A guide to the less desirable aspects of deep learning environment configurations
https://towardsdatascience.com/build-your-own-robust-deep-learning-environment-in-minutes-354cf140a5a6
292 743
A Gentle Introduction to Hypotheses in Machine Learning
What is a Hypothesis in Machine Learning?
https://machinelearningmastery.com/what-is-a-hypothesis-in-machine-learning/
292 743
Fundamentals of Machine Learning (Part 3)
https://towardsdatascience.com/fundamentals-of-machine-learning-part-3-b305933f00cd?source=collection_home---4------0---------------------
292 743
Как можно упростить и ускорить вычисление нейронной сети прямого распространения
https://habr.com/ru/hub/artificial_intelligence/
292 743
Probability Theory for Machine/Deep Learning
https://towardsdatascience.com/probability-theory-for-deep-learning-9551b9255cf0?source=collection_home---4------3---------------------
292 743
All the Steps to Build your first Image Classifier (with code)
From creating datasets to testing your program accuracy
https://medium.com/@artux51/all-the-steps-to-build-your-first-image-classifier-with-code-cf244b015799
292 743
Recurrent Neural Networks
Remembering what’s important
https://towardsdatascience.com/recurrent-neural-networks-d4642c9bc7ce
292 743
Работа с Big Data в «МегаФон»
Директор по аналитике больших данных компании «МегаФон» рассказывает, как вырасти в профессионала в области big data и как именно работает с ними сотовый оператор.
https://www.youtube.com/watch?v=C6zSSWoOUOw«МегаФон»
292 743
Boosting: Is It Always The Best Option?
https://towardsdatascience.com/boosting-is-it-always-the-best-option-ce624a359c19
292 743
PyTorch Machine Learning Tutorial - Machine Learning with Python and PyTorch
https://www.youtube.com/watch?v=TB-G1KqRb5o
