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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 292 964 subscribers, ranking 328 in the Technologies & Applications category and 1 278 in the Russia region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 292 964 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.

292 964
Subscribers
-18724 hours
-1 3257 days
-6 31430 days
Posts Archive
Disentangling Disentanglement in Variational Autoencoders Article.: http://proceedings.mlr.press/v97/mathieu19a.html

Hierarchical Representation in Neural Language Models: Suppression and Recovery of Expectations https://arxiv.org/abs/1906.04068

Серверы с GPU от 90 рублей в час! 🔥 Облачные вычисления на базе графических ускорителей Tesla V100 отлично подойдут для маши
Серверы с GPU от 90 рублей в час! 🔥 Облачные вычисления на базе графических ускорителей Tesla V100 отлично подойдут для машинного обучения, анализа данных и высокопроизводительных вычислений. 👀 Подробности по ссылке: http://bit.ly/2KEZPcm

Deep Learning Frameworks 2019 | Which Deep Learning Framework To Use | Deep Learning https://www.youtube.com/watch?v=6ryPbOfz03U

One-Shot Learning with Siamese Networks, Contrastive Loss, and Triplet Loss for Face Recognition https://machinelearningmastery.com/one-shot-learning-with-siamese-networks-contrastive-and-triplet-loss-for-face-recognition/

Functional Adversarial Attacks Article: https://arxiv.org/abs/1906.00001

Отличаем символы от мусора: как построить устойчивые нейросетевые модели в задачах OCR https://habr.com/ru/company/abbyy/blog/449524/

Welcome to TensorWatch TensorWatch is a debugging and visualization tool designed for deep learning and reinforcement learning from Microsoft Research. It works in Jupyter Notebook to show real-time visualizations of your machine learning training and perform several other key visualizations of your models and data. https://github.com/microsoft/tensorwatch/

Free COURSE. CS Deep Reinforcement Learning UC Berkeley Video Lectures: https://www.youtube.com/playlist?list=PLkFD6_40KJIxJM.. Lecture Material: http://rail.eecs.berkeley.edu/deeprlcourse/

Introducing TensorNetwork, an Open Source Library for Efficient Tensor Calculations http://ai.googleblog.com/2019/06/introducing-tensornetwork-open-source.html

DeepMind Made a Math Test For Neural Networks https://www.youtube.com/watch?v=f9z1I_81_Q4