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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 292 388 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
PySyft PySyft is a Python library for secure, private Deep Learning. PySyft decouples private data from model training, using Multi-Party Computation (MPC) within PyTorch. https://github.com/OpenMined/PySyft

Kaggle Competition — Image Classification How to build a CNN model that can predict the classification of the input images using transfer learning https://towardsdatascience.com/kaggle-competition-image-classification-676dee6c0f23

PyTorch implementation of Google AI's BERT model with a script to load Google's pre-trained models https://github.com/huggingface/pytorch-pretrained-BERT

The MAME RL Algorithm Training Toolkit This Python library has the to potential to train your reinforcement learning algorithm on almost any arcade game. It is currently available on Linux systems and works as a wrapper around MAME. The toolkit allows your algorithm to step through gameplay while recieving the frame data and internal memory address values for tracking the games state, along with sending actions to interact with the game. https://github.com/M-J-Murray/MAMEToolkit

Дорисовывание лиц с помощью машинного обучения https://habr.com/post/428756/

Building client routing / semantic search and clustering arbitrary external corpuses at Profi.ru https://habr.com/post/428674/

Facebook open sourced Horizon, an end-to-end applied reinforcement learning platform built on #PyTorch 1.0. Horizon uses RL to optimize systems in large-scale production environments and we're excited to make it accessible to anyone using #RL at scale. https://code.fb.com/ml-applications/horizon/

Introducing AdaNet: Fast and Flexible AutoML with Learning Guarantees https://ai.googleblog.com/2018/10/introducing-adanet-fast-and-flexible.html