en
Feedback
Machinelearning

Machinelearning

Open in Telegram

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

Show more

📈 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
How to Create a Random-Split, Cross-Validation, and Bagging Ensemble for Deep Learning in Keras https://machinelearningmastery.com/how-to-create-a-random-split-cross-validation-and-bagging-ensemble-for-deep-learning-in-keras/

30 Data Science Punchlines A holiday reading list condensed into 30 quotes https://towardsdatascience.com/data-science-conversation-starters-84affd2347f6

10 Exciting Ideas of 2018 in NLP http://ruder.io/10-exciting-ideas-of-2018-in-nlp/

Best NLP articles explanation https://jalammar.github.io/illustrated-bert/

Facebook has released #PyText — new framework on top of #PyTorch. This framework is build to make it easier for developers to build #NLP models. https://code.fb.com/ai-research/pytext-open-source-nl.. Github: https://github.com/facebookresearch/pytext

How to Stop Training Deep Neural Networks At the Right Time Using Early Stopping https://machinelearningmastery.com/how-to-stop-training-deep-neural-networks-at-the-right-time-using-early-stopping/

A Gentle Introduction to Early Stopping to Avoid Overtraining Deep Learning Neural Network Models https://machinelearningmastery.com/early-stopping-to-avoid-overtraining-neural-network-models/

Great took for neural network, deep learning and machine learning models visualization. https://github.com/lutzroeder/netron

Super VIP Cheatsheet: Deep Learning