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 292 388 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 1 290 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 292 388 名订阅者。
根据 08 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 -6 274，过去 24 小时变化为 -221，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 7.46%。内容发布后 24 小时内通常能获得 5.47% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 21 812 次浏览，首日通常累积 16 003 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 09 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。
292 388
订阅者
-22124 小时
-1 3547 天
-6 27430 天
帖子存档
292 350
Dimensionality Reduction For Dummies — Part 2: Laying The Bricks
https://towardsdatascience.com/data-science/home
292 350
Playing Mortal Kombat with TensorFlow.js. Transfer learning and data augmentation
https://blog.mgechev.com/2018/10/20/transfer-learning-tensorflow-js-data-augmentation-mobile-net/
292 350
How to analyze “Learning”: Short tour of Computational Learning Theory
https://towardsdatascience.com/how-to-analyze-learning-short-tour-of-computational-learning-theory-9d93b15fc3e5
292 350
Curiosity and Procrastination in Reinforcement Learning
https://ai.googleblog.com/2018/10/curiosity-and-procrastination-in.html
292 350
Deep Learning and Reinforcement Learning Summer School, Toronto 2018
video:
http://videolectures.net/DLRLsummerschool2018_toronto/
292 350
How linear algebra is applied in machine learning.
When you study an abstract subject like linear algebra, you may wonder: why do you need all these vectors and matrices? Well, if you study it with the purpose of doing ML, this is the answer for you: http://amp.gs/vtWx
292 350
Building Machine Learning Model From Unstructured Data
https://towardsdatascience.com/building-machine-learning-model-from-unstructured-data-dd2d0263f1db
292 350
Digging into Airbnb data: reviews sentiments, superhosts, and prices prediction (part1)
Example of #AirBnB data research
Link: https://towardsdatascience.com/digging-into-airbnb-data-reviews-sentiments-superhosts-and-prices-prediction-part1-6c80ccb26c6a
292 350
mmdetection
mmdetection is an open source object detection toolbox based on PyTorch. It is a part of the open-mmlab project developed by Multimedia Laboratory, CUHK.
Major features
- Modular Design
One can easily construct a customized object detection framework by combining different components.
- Support of multiple frameworks out of box
The toolbox directly supports popular detection frameworks, e.g. Faster RCNN, Mask RCNN, RetinaNet, etc.
- Efficient
All basic bbox and mask operations run on GPUs now. The training speed is about 5% ~ 20% faster than Detectron for different models.
- State of the art
This was the codebase of the MMDet team, who won the COCO Detection 2018 challenge.
https://github.com/open-mmlab/mmdetection
292 350
"Mathematics for Machine Learning": drafts for all chapters now available
https://mml-book.github.io/ https://www.reddit.com/r/MachineLearning/comments/9lzabc/p_mathematics_for_machine_learning_drafts_for_all/
292 350
SOTAWHAT - A script to keep track of state-of-the-art AI research
https://huyenchip.com/2018/10/04/sotawhat.html
https://github.com/chiphuyen/sotawhat.
292 350
Top AI Interview Questions & Answers — Acing the AI Interview
https://medium.com/acing-ai/top-ai-interview-questions-answers-acing-the-ai-interview-61bf52ca34d4
292 350
Most recent version of Andrew Ng’s book Machine Learning Yearning
Link: https://gallery.mailchimp.com/dc3a7ef4d750c0abfc19202a3/files/5dd91615-3b3f-4f5d-bbfb-4ebd8608d330/Ng_MLY01_13.pdf
292 350
Intro to Deep Learning with PyTorch https://in.udacity.com/course/deep-learning-pytorch--ud188
292 350
Introduction to forecasting with FB Prophet https://www.interviewqs.com/ddi_code_snippets/prophet_intro
