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 276 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 293 167 subscribers, ranking 326 in the Technologies & Applications category and 1 276 in the Russia region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 293 167 subscribers.

According to the latest data from 04 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -6 366 over the last 30 days and by -131 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 7.35%. Within the first 24 hours after publication, content typically collects 5.62% reactions from the total number of subscribers.
Post reach: On average, each post receives 21 569 views. Within the first day, a publication typically gains 16 480 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 168.
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 05 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.

293 167

Subscribers

-13124 hours

-1 4647 days

-6 36630 days

21 569

Post views

~ 16 48024 hours

~ 18 29048 hours

7.35%

Engagement rate

~ 6

Posts per day

Ads index

beta

Posts Archive

293 172

17th September In Moscow MegaFon office will host another meetup. Speakers from Mail.Ru, Altinity, Couchbase and MegaFon will talk about Statefull in Kubernetes. Free admission. For details and registration : https://pao-megafon--org.timepad.ru/event/1056036/

293 172

Learning Cross-Modal Temporal Representations from Unlabeled Videos http://ai.googleblog.com/2019/09/learning-cross-modal-temporal.html

293 172

Recursive Sketches for Modular Deep Learning http://ai.googleblog.com/2019/09/recursive-sketches-for-modular-deep.html

293 172

📝 The paper: Adversarial Examples Are Not Bugs, They Are Features video: https://www.youtube.com/watch?v=AOZw1tgD8dA available here: http://gradientscience.org/adv/ article: https://distill.pub/2019/advex-bugs-discussion/

293 172

Assessing the Quality of Long-Form Synthesized Speech http://ai.googleblog.com/2019/09/assessing-quality-of-long-form.html

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DeepMind's OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games. code: https://github.com/deepmind/open_spiel article: https://arxiv.org/abs/1908.09453

293 172

How to Develop and Evaluate Naive Classifier Strategies Using Probability https://machinelearningmastery.com/how-to-develop-and-evaluate-naive-classifier-strategies-using-probability/

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💬 Announcing Two New Natural Language Dialog Datasets https://ai.googleblog.com/2019/09/announcing-two-new-natural-language.html Coached Conversational Preference Elicitation A dataset consisting of 502 dialogs with 12,000 annotated utterances between a user and an assistant discussing movie preferences in natural language. https://ai.google/tools/datasets/coached-conversational-preference-elicitation Accessing the Taskmaster-1 dataset The full Taskmaster-1 dialog dataset has total 13,215 dialogs with 7708 written and 5507 spoken. https://storage.googleapis.com/dialog-data-corpus/TASKMASTER-1-2019/landing_page.html

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Pytorch implementation of the paper "Class-Balanced Loss Based on Effective Number of Samples» https://github.com/vandit15/Class-balanced-loss-pytorch Class-Balanced Loss Based on Effective Number of Samples https://github.com/richardaecn/class-balanced-loss

293 172

Introducing Neural Structured Learning in TensorFlow https://medium.com/tensorflow/introducing-neural-structured-learning-in-tensorflow-5a802efd7afd Neural Structured Learning: Training with Structured Signals Article: https://www.tensorflow.org/neural_structured_learning Code: https://github.com/tensorflow/neural-structured-learning

293 172

Giving Lens New Reading Capabilities in Google Go http://ai.googleblog.com/2019/09/giving-lens-new-reading-capabilities-in.html

293 172

A Gentle Introduction to Jensen’s Inequality https://machinelearningmastery.com/a-gentle-introduction-to-jensens-inequality/

293 172

Rules of Machine Learning by Google Best Practices for ML Engineering https://developers.google.com/machine-learning/guides/rules-of-ml/

293 172

Adapt or Get Left Behind: Domain Adaptation through BERT Language Model Finetuning for Aspect-Target Sentiment Classification https://arxiv.org/abs/1908.11860

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A Gentle Introduction to Generative Adversarial Network Loss Functions https://machinelearningmastery.com/generative-adversarial-network-loss-functions/

293 172

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

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Тестим: профессия ML-разработчик 3 сентября в 19:00 Три человека разных профессий впервые напишут собственный сервис, основанный на машинном обучении. Вы тоже сможете. Присоединяйтесь: https://clc.to/85qbFg Как это устроено? — Эмиль Магеррамов, COO в EORA Data Lab и ведущий преподаватель специализации «Data Science» в SkillFactory — короткая видеолекция и инструкция по установке необходимых приложений для работы — час интенсива по Machine learning в режиме реального времени с преподавателем и другими студентами — ваш собственный сервис для определения спама, основанный на машинном обучении, уже к вечеру. Регистрируйтесь и попробуйте свои силы в Machine learning: https://clc.to/85qbFg

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🔥Finally, AI-Based Painting is here! #GANPaint video: https://www.youtube.com/watch?v=IqHs_DkmDVo Semantic Photo Manipulation with a Generative Image Prior paper: http://ganpaint.io/

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🚀 Introducing TF-GAN: A lightweight GAN library for TensorFlow 2.0 Tensorflow blog: https://medium.com/tensorflow/introducing-tf-gan-a-lightweight-gan-library-for-tensorflow-2-0-36d767e1abae Code: https://github.com/tensorflow/gan Free course: https://developers.google.com/machine-learning/gan/ Paper: https://arxiv.org/abs/1805.08318

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PyTorch Examples A repository showcasing examples of using PyTorch https://github.com/pytorch/examples