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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 292 839 subscribers, ranking 328 in the Technologies & Applications category and 1 282 in the Russia region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 292 839 subscribers.

According to the latest data from 06 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -6 314 over the last 30 days and by -187 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 7.37%. Within the first 24 hours after publication, content typically collects 5.45% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 21 579 views. Within the first day, a publication typically gains 15 979 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 07 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.

292 839
Subscribers
-18724 hours
-1 3257 days
-6 31430 days
Posts Archive
Mini Course in Deep Learning with PyTorch for AIMS https://github.com/Atcold/pytorch-Deep-Learning-Minicourse

MorphNet: Towards Faster and Smaller Neural Networks http://ai.googleblog.com/2019/04/morphnet-towards-faster-and-smaller.html

YoloV3 Implemented in TensorFlow 2.0 https://github.com/zzh8829/yolov3-tf2

Take Your Best Selfie Automatically, with Photobooth on Pixel 3 http://ai.googleblog.com/2019/04/take-your-best-selfie-automatically.html

Week 8 (part c) CS294-158 Deep Unsupervised Learning (4/3/19) -- Ilya Sutskever https://www.youtube.com/watch?v=X-B3nAN7YRM

Основы Natural Language Processing для текста https://habr.com/ru/company/Voximplant/blog/446738/

How to Use Test-Time Augmentation to Improve Model Performance for Image Classification https://machinelearningmastery.com/how-to-use-test-time-augmentation-to-improve-model-performance-for-image-classification/

How to Configure Image Data Augmentation When Training Deep Learning Neural Networks https://machinelearningmastery.com/how-to-configure-image-data-augmentation-when-training-deep-learning-neural-networks/

Review: Residual Attention Network — Attention-Aware Features (Image Classification) https://towardsdatascience.com/review-residual-attention-network-attention-aware-features-image-classification-7ae44c4f4b8

How to Load Large Datasets From Directories for Deep Learning with Keras https://machinelearningmastery.com/how-to-load-large-datasets-from-directories-for-deep-learning-with-keras/

Recurrent Neural Networks in Python https://www.youtube.com/watch?v=kZPRyeiaBnc

Make Money with Tensorflow 2.0 https://www.youtube.com/watch?v=WS9Nckd2kq0

Zero to Cohort Analysis in 60 Minutes https://data.valorep.com/posts/p1_zero_to_cohorts/

How to Get Started With Deep Learning for Computer Vision (7-Day Mini-Course) https://machinelearningmastery.com/how-to-get-started-with-deep-learning-for-computer-vision-7-day-mini-course/

How to Load and Visualize Standard Computer Vision Datasets With Keras https://machinelearningmastery.com/how-to-load-and-visualize-standard-computer-vision-datasets-with-keras/

Introduction to Tensorflow 2.0 | Tensorflow 2.0 Features and Changes https://www.youtube.com/watch?v=3O-5DuqKaRo