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Machinelearning

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 292 747 subscribers, ranking 328 in the Technologies & Applications category and 1 291 in the Russia region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 292 747 subscribers.

According to the latest data from 07 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -6 317 over the last 30 days and by -209 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 7.45%. Within the first 24 hours after publication, content typically collects 5.46% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 21 817 views. Within the first day, a publication typically gains 15 977 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 160.
  • 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 08 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.

292 747
Subscribers
-20924 hours
-1 3687 days
-6 31730 days
Posts Archive
DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learning https://www.nature.com/articles/s41598-018-38343-3

Реализация моделей seq2seq в Tensorflow https://habr.com/ru/post/440472/

GANimation: Anatomically-aware Facial Animation from a Single Image https://github.com/albertpumarola/GANimation

Introducing PlaNet: A Deep Planning Network for Reinforcement Learning https://ai.googleblog.com/2019/02/introducing-planet-deep-planning.html

Russian AI Cup 2018, история 9 места https://habr.com/ru/post/440574/

Box Convolution Layer for ConvNets This is a PyTorch implementation of the box convolution layer as introduced in the 2018 NeurIPS paper: https://github.com/shrubb/box-convolutions

Nature Machine Intelligence

Introduction to gradient boosting on decision trees with Catboost Today I would like to share my experience with open source machine learning library, based on gradient boosting on decision trees, developed by Russian search engine company — Yandex. https://towardsdatascience.com/introduction-to-gradient-boosting-on-decision-trees-with-catboost-d511a9ccbd14

The Ancient Secrets of Computer Vision University of Washington. Free course This class is a general introduction to computer vision. It covers standard techniques in image processing like filtering, edge detection, stereo, flow, etc. , as well as newer, machine-learning based computer vision. https://pjreddie.com/courses/computer-vision/

A Simple Baseline for Bayesian Deep Learning https://github.com/wjmaddox/swa_gaussian

Автономная езда по тротуару посредством OpenCV и Tensorflow https://habr.com/ru/post/439928/