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1 278 技术与应用328...

📈 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 292 964 名订阅者，在 技术与应用 类别中位列第 328，并在 俄罗斯 地区排名第 1 278 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 292 964 名订阅者。

根据 06 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 -6 314，过去 24 小时变化为 -187，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 7.37%。内容发布后 24 小时内通常能获得 5.45% 的反应，占订阅者总量。
帖子覆盖： 每篇帖子平均可获得 21 579 次浏览，首日通常累积 15 979 次浏览。
互动与反馈： 受众积极参与，单帖平均反应数为 159。
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 07 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。

292 964

订阅者

-18724 小时

-1 3257 天

-6 31430 天

21 579

帖子浏览量

~ 15 97924 小时

~ 17 85348 小时

7.37%

参与率

~ 6

每日帖子数

Ads index

beta

帖子存档

292 956

A General Decoupled Learning Framework for Parameterized Image Operators https://arxiv.org/abs/1907.05852

292 956

New fast.ai course: A Code-First Introduction to Natural Language Processing https://www.fast.ai/2019/07/08/fastai-nlp/ Github: https://github.com/fastai/course-nlp Videos: https://www.youtube.com/playlist?list=PLtmWHNX-gukKocXQOkQjuVxglSDYWsSh9

292 956

Postuf — продуктовая компания, которая занимается разработкой приложения, позволяющего узнать всю информацию о человеке из сети. Вводите имя, загружаете фото и получаете информацию об увлечениях, круге общения, последних совершённых действиях — всё, что только можно узнать о человеке из открытых источников. Сейчас они ищут разработчиков на Android, iOS и бэкенд (PHP, MySQL, Redis). Условия комфортные — офис в центре Москвы (ст.м. Чкаловская), Macbook Pro для работы, бесплатные обеды и ежегодный рост з/п на 20к. Подробнее о компании и вакансии: https://postuf.com/

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How to Implement Wasserstein Loss for Generative Adversarial Networks https://machinelearningmastery.com/how-to-implement-wasserstein-loss-for-generative-adversarial-networks/

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Simple Deep Q Network w/Pytorch: https://youtu.be/UlJzzLYgYoE Reinforcement Learning Crash Course: https://youtu.be/sOiNMW8k4T0 Policy Gradients w/Tensorflow: https://youtu.be/UT9pQjVhcaU Deep Q Learning w/Tensorflow https://youtu.be/3Ggq_zoRGP4 Code Your Own RL Environments https://youtu.be/vmrqpHldAQ0 How to Spec a Deep Learning PC: https://youtu.be/xsnVlMWQj8o Deep Q Learning w/ Pytorch: https://youtu.be/RfNxXlO6BiA Machine Learning Freelancing https://youtu.be/6M04ZTLE_O4 Code from video: https://github.com/philtabor/Youtube-Code-Repository

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Learning to learn with quantum neural networks via classical neural networks https://arxiv.org/abs/1907.05415

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An implementation of the BERT-DST: Scalable End-to-End Dialogue State Tracking with Bidirectional Encoder Representations from Transformer (Interspeech 2019) Article: https://arxiv.org/pdf/1907.03040.pdf Github: https://github.com/guanlinchao/bert-dst

292 956

How to Code the GAN Training Algorithm and Loss Functions https://machinelearningmastery.com/how-to-code-the-generative-adversarial-network-training-algorithm-and-loss-functions/

292 956

Multilingual Universal Sentence Encoder for Semantic Retrieval http://ai.googleblog.com/2019/07/multilingual-universal-sentence-encoder.html

292 956

TRFL a library of reinforcement learning building blocks By the Research Engineering team at DeepMind: https://github.com/deepmind/trfl

292 956

Advancing Semi-supervised Learning with Unsupervised Data Augmentation http://ai.googleblog.com/2019/07/advancing-semi-supervised-learning-with.html

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A Tour of Generative Adversarial Network Models https://machinelearningmastery.com/tour-of-generative-adversarial-network-models/

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Bayesian deep learning with hierarchical prior: Predictions from limited and noisy data Article: https://arxiv.org/abs/1907.04240 PDF: https://arxiv.org/pdf/1907.04240.pdf

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Predicting the Generalization Gap in Deep Neural Networks http://ai.googleblog.com/2019/07/predicting-generalization-gap-in-deep.html

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Neural Architecture Search at CVPR 2019 https://drsleep.github.io/NAS-at-CVPR-2019/

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How to Identify and Diagnose GAN Failure Modes https://machinelearningmastery.com/practical-guide-to-gan-failure-modes/

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Rank-consistent Ordinal Regression for Neural Networks Article: https://arxiv.org/abs/1901.07884 PyTorch: https://github.com/Raschka-research-group/coral-cnn

292 956

Literature of Deep Learning for Graphs This is a paper list about deep learning for graphs. https://github.com/DeepGraphLearning/LiteratureDL4Graph

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Check the data science channel there you will find a lot of articles, links and advanced researches . Join and learn hot topics of data science @opendatascience

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How to Develop a Conditional GAN (cGAN) From Scratch Generative Adversarial Networks, or GANs, are an architecture for training generative models, such as deep convolutional neural networks for generating images.