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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 292 329 subscribers, ranking 328 in the Technologies & Applications category and 1 292 in the Russia region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 292 329 subscribers.

According to the latest data from 09 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -6 200 over the last 30 days and by -193 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 7.33%. Within the first 24 hours after publication, content typically collects 5.53% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 21 439 views. Within the first day, a publication typically gains 16 173 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 154.
  • 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 10 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.

292 329
Subscribers
-19324 hours
-1 2987 days
-6 20030 days
Posts Archive
Complete code examples for Machine Translation with Attention, Image Captioning, Text Generation, and DCGAN implemented with tf.keras and eager execution “Complete code examples for Machine Translation with Attention, Image Captioning, Text Generation…” https://medium.com/tensorflow/complete-code-examples-for-machine-translation-with-attention-image-captioning-text-generation-51663d07a63d #tensorflow #tutorial

A Style-Aware Content Loss for Real-time HD Style Transfer https://compvis.github.io/adaptive-style-transfer/

FusionGAN-Tensorflow Simple Tensorflow implementation of FusionGAN (CVPR 2018, https://arxiv.org/abs/1804.07455) https://github.com/taki0112/FusionGAN-Tensorflow