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Machinelearning

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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

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📈 Analytical overview of Telegram channel Machinelearning

Channel Machinelearning (@ai_machinelearning_big_data) in the Russian language segment is an active participant. Currently, the community unites 292 388 subscribers, ranking 328 in the Technologies & Applications category and 1 290 in the Russia region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 292 388 subscribers.

According to the latest data from 08 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -6 274 over the last 30 days and by -221 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 7.46%. Within the first 24 hours after publication, content typically collects 5.47% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 21 812 views. Within the first day, a publication typically gains 16 003 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 159.
  • Thematic interests: Content is focused on key topics such as openai, claude, api, gemini, контекст.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
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

Thanks to the high frequency of updates (latest data received on 09 July, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.

292 388
Subscribers
-22124 hours
-1 3547 days
-6 27430 days
Posts Archive
Dimensionality Reduction For Dummies — Part 2: Laying The Bricks https://towardsdatascience.com/data-science/home

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/

How to analyze “Learning”: Short tour of Computational Learning Theory https://towardsdatascience.com/how-to-analyze-learning-short-tour-of-computational-learning-theory-9d93b15fc3e5

Curiosity and Procrastination in Reinforcement Learning https://ai.googleblog.com/2018/10/curiosity-and-procrastination-in.html

Deep Learning and Reinforcement Learning Summer School, Toronto 2018 video: http://videolectures.net/DLRLsummerschool2018_toronto/

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

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

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

Google 2019 research internships https://t.co/rxmLEPLsir

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.

Top AI Interview Questions & Answers — Acing the AI Interview https://medium.com/acing-ai/top-ai-interview-questions-answers-acing-the-ai-interview-61bf52ca34d4

Introduction to forecasting with FB Prophet https://www.interviewqs.com/ddi_code_snippets/prophet_intro