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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 292 747 subscribers, ranking 328 in the Technologies & Applications category and 1 291 in the Russia region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 292 747 subscribers.

According to the latest data from 07 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -6 317 over the last 30 days and by -209 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 7.45%. Within the first 24 hours after publication, content typically collects 5.46% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 21 817 views. Within the first day, a publication typically gains 15 977 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 160.
  • 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 08 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.

292 747
Subscribers
-20924 hours
-1 3687 days
-6 31730 days
Posts Archive
The Hundred-Page Machine Learning Book http://themlbook.com/wiki/doku.php

Reinforcement Learning Tutorial | Reinforcement Learning Example Using Python https://www.youtube.com/watch?v=LzaWrmKL1Z4

University of California, Berkeley full course 2018 This course will introduce the basic ideas and techniques underlying the design of intelligent computer systems. A specific emphasis will be on the statistical and decision-theoretic modeling paradigm. By the end of this course, you will have built autonomous agents that efficiently make decisions in fully informed, partially observable and adversarial settings https://inst.eecs.berkeley.edu/~cs188/fa18/

Using the latest advancements in deep learning to predict stock price movements https://medium.com/@borisborev/aifortrading-2edd6fac689d

Изучаешь Data Science? Такого ты еще не видел!@mommyscience - авторский канал, на котором не бывает скучно, только годная информация, челенджи и рекомендации. Качай свой скилл вместе с нами! Подписавшись, ты получишь доступ к постоянно пополняющейся базе знаний, а именно: ✔️ Разбор реальных задач ✔️ Рекомендации и советы по обучению ✔️ Внутренние соревнования и викторины ✔️ Ссылки на полезные материалы ✔️ Участие в соревнованиях на Kaggle и многое другое https://t.me/mommyscience

Reinforcement learning without gradients: evolving agents using Genetic Algorithms https://towardsdatascience.com/reinforcement-learning-without-gradients-evolving-agents-using-genetic-algorithms-8685817d84f

Python Anaconda for Deep Learning, Keras and Tensorflow (Module 1, Part 3) https://www.youtube.com/watch?v=uOMhboAnVNk

Top 10 IPython Notebook Tutorials for Data Science and Machine Learning List mostly for beginners. Link: https://www.kdnuggets.com/2016/04/top-10-ipython-nb-tutorials.html #novice #beginner #ipython #jupyter

Creating voice assistant for games (tutorial for FIFA) Play games with voice commands using a Deep Learning powered wake-word detection engine https://towardsdatascience.com/creating-voice-assistant-for-games-tutorial-for-fifa-71cfbe428bd1

Impact of Dataset Size on Deep Learning Model Skill And Performance Estimates https://machinelearningmastery.com/impact-of-dataset-size-on-deep-learning-model-skill-and-performance-estimates/

How to Develop a Stacking Ensemble for Deep Learning Neural Networks in Python With Keras https://machinelearningmastery.com/stacking-ensemble-for-deep-learning-neural-networks/

Explained: A Style-Based Generator Architecture for GANs - Generating and Tuning Realistic Artificial Faces https://towardsdatascience.com/explained-a-style-based-generator-architecture-for-gans-generating-and-tuning-realistic-6cb2be0f431

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/