Machinelearning

前往频道在 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

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网络:Machinelearning 俄罗斯1 276 技术与应用326...

📈 Telegram 频道 Machinelearning 的分析概览

频道 Machinelearning (@ai_machinelearning_big_data) 俄语语言赛道中的是活跃参与者。目前社区聚集了 293 260 名订阅者，在 技术与应用 类别中位列第 326，并在 俄罗斯 地区排名第 1 276 位。

📊 受众指标与增长动态

自 невідомо 创建以来，项目保持高速增长，吸引了 293 260 名订阅者。

根据 04 七月, 2026 的最新数据，频道保持稳定运转。过去 30 天订阅人数变化为 -6 366，过去 24 小时变化为 -131，整体触达仍然可观。

认证状态： 未认证
互动率 (ER)： 平均受众互动率为 7.35%。内容发布后 24 小时内通常能获得 5.62% 的反应，占订阅者总量。
帖子覆盖： 每篇帖子平均可获得 21 569 次浏览，首日通常累积 16 480 次浏览。
互动与反馈： 受众积极参与，单帖平均反应数为 168。
主题关注点： 内容集中在 openai, claude, api, gemini, контекст 等核心主题上。

📝 描述与内容策略

作者将该频道定位为表达主观观点的平台：
“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”

凭借高频更新（最新数据采集于 05 七月, 2026），频道始终保持新鲜度与高覆盖。分析显示受众积极互动，使其成为 技术与应用 类别中的关键影响点。

293 260

订阅者

-13124 小时

-1 4647 天

-6 36630 天

21 569

帖子浏览量

~ 16 48024 小时

~ 18 29048 小时

7.35%

参与率

~ 6

每日帖子数

Ads index

beta

帖子存档

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

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📝 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

293 172

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/

293 172

💬 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

293 172

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