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

Network:Machinelearning Russia1 283 Technologies & Applications326...

📈 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 293 306 subscribers, ranking 326 in the Technologies & Applications category and 1 283 in the Russia region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 293 306 subscribers.

According to the latest data from 03 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -6 469 over the last 30 days and by -218 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 7.32%. Within the first 24 hours after publication, content typically collects 5.77% reactions from the total number of subscribers.
Post reach: On average, each post receives 21 487 views. Within the first day, a publication typically gains 16 937 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 169.
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 04 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.

293 306

Subscribers

-21824 hours

-1 5287 days

-6 46930 days

21 487

Post views

~ 16 93724 hours

~ 18 77448 hours

7.32%

Engagement rate

~ 6

Posts per day

Ads index

beta

Posts Archive

293 292

How to Transform Target Variables for Regression With Scikit-Learn https://machinelearningmastery.com/how-to-transform-target-variables-for-regression-with-scikit-learn/

293 292

BMW shares AI algorithms used in production, available on GitHub https://www.bmwblog.com/2019/12/13/bmw-shares-ai-algorithms-used-in-production-available-on-github/ Github : https://github.com/BMW-InnovationLab

293 292

Quality-Diversity optimisation algorithms https://quality-diversity.github.io/ Code: https://gitlab.com/leo.cazenille/qdpy

293 292

Develop Smaller Speech Recognition Models with NVIDIA’s NeMo Framework https://devblogs.nvidia.com/develop-smaller-speech-recognition-models-with-nvidias-nemo-framework/ Github: https://github.com/NVIDIA/NeMo https://ngc.nvidia.com/catalog/models/nvidia:quartznet15x5

293 292

Tune Hyperparameters for Classification Machine Learning Algorithms https://machinelearningmastery.com/hyperparameters-for-classification-machine-learning-algorithms/

293 292

Model-Based Reinforcement Learning: Theory and Practice https://bair.berkeley.edu/blog/2019/12/12/mbpo/

293 292

Хочешь разобраться, кто такой Data Scientist? Узнать, что такое машинное обучение? Добро пожаловать на курс «Профессия Data Scientist!». Пройдя его, ты получишь востребованную работу, с интересными задачами, да и зарплата неплохая. 👉 Все подробности по ссылке: https://clc.to/5c37tA Что ты получишь: 📌 целых 300 часов онлайн-обучения; 📌 2 специальности в одной программе; 📌 реальные задачи и много практики; 📌 гарантированное трудоустройство после прохождения курса. Учись 6 месяцев бесплатно! Первый платеж соверши аж через полгода. Для всех студентов подарок — 2 месяца английского языка бесплатно! 🎁 В новый год с новыми знаниями! Skillbox дарит огромную скидку 50% в декабре!

293 292

Fairness Indicators: Scalable Infrastructure for Fair ML Systems http://ai.googleblog.com/2019/12/fairness-indicators-scalable.html Github: https://github.com/tensorflow/fairness-indicators Blog: https://blog.tensorflow.org/2019/12/fairness-indicators-fair-ML-systems.html

293 292

KingSoft WPS: document image dewarping based on TensorFlow https://blog.tensorflow.org/2019/12/kingsoft-wps-document-image-dewarping.html

293 292

A practical approach to machine learning https://github.com/practicalAI/practicalAI

293 292

NeurIPS 2019 Neural Information Processing Systems (NeurIPS) https://slideslive.com/neurips

293 292

Develop an Intuition for Bayes Theorem With Worked Examples https://machinelearningmastery.com/intuition-for-bayes-theorem-with-worked-examples/

293 292

PyTorch adds new tools and libraries, welcomes https://pytorch.org/blog/pytorch-adds-new-tools-and-libraries-welcomes-preferred-networks-to-its-community/ NeurIPS 2019 Expo Workshop https://nips.cc/ExpoConferences/2019/schedule?workshop_id=16 PyTorch Elastic : https://github.com/pytorch/elastic @ai_machinelearning_big_data

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Свежая подборка из мира новостей по искусственному интеллекту, Big Data и машинному обучению. Переходи по ссылке и будь в курсе всего, что происходит в России и мире. Не оставайся в стороне от историй, за которыми будущее. https://t.me/bolshiedannye

293 292

A new framework for large-scale training of state-of-the-art visual classification models https://ai.facebook.com/blog/a-new-framework-for-large-scale-training-of-state-of-the-art-visual-classification-models/ Github: https://github.com/facebookresearch/ClassyVision Multi-modal Research to Production with PyTorch and Facebook https://nips.cc/ExpoConferences/2019/schedule?workshop_id=16 https://classyvision.ai/

293 292

Understanding Transfer Learning for Medical Imaging https://ai.googleblog.com/2019/12/understanding-transfer-learning-for.html Article: https://arxiv.org/abs/1902.07208

293 292

Deep Double Descent https://openai.com/blog/deep-double-descent/ Deep Double Descent: Where Bigger Models and More Data Hurt https://arxiv.org/abs/1912.02292

293 292

Netflix open-sources its Python Framework ‘Metaflow’ for building and managing data science projects https://www.marktechpost.com/2019/12/04/netflix-open-sources-its-python-framework-metaflow-for-building-and-managing-data-science-projects/ Github: https://github.com/Netflix/metaflow Documentation: https://docs.metaflow.org/

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Training multi-agent AI systems to solve complex tasks through cooperation https://ai.facebook.com/blog/using-multi-agent-reinforcement-learning-to-improve-collaboration/ full paper: https://arxiv.org/abs/1910.08809 code: https://github.com/TorchCraft/TorchCraftAI/tree/targeting

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Python for Scientists @ai_machinelearning_big_data