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1 291 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 292 747 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 1 291 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 292 747 名订阅者。

根据 07 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 -6 317，过去 24 小时变化为 -209，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 7.45%。内容发布后 24 小时内通常能获得 5.46% 的反应，占订阅者总量。
帖子覆盖： 每篇帖子平均可获得 21 817 次浏览，首日通常累积 15 977 次浏览。
互动与反馈： 受众积极参与，单帖平均反应数为 160。
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 08 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。

292 747

订阅者

-20924 小时

-1 3687 天

-6 31730 天

21 817

帖子浏览量

~ 15 97724 小时

~ 17 79548 小时

7.45%

参与率

~ 6

每日帖子数

Ads index

beta

帖子存档

292 752

How to Calculate Precision, Recall, F1, and More for Deep Learning Models https://machinelearningmastery.com/how-to-calculate-precision-recall-f1-and-more-for-deep-learning-models/

292 752

The Startups Disrupting Retail at The New Retail Conference by Sistema_VC Face recognition for retail, AI-driven windows and stocks, personalised offer for each customer in the store as if they were shopping online. Register to know how all these functions in the modern shops. Among speakers there are founders of successful startups from USA, Israel, UK. Place: Moscow, Tablica co-working, Novoslobodskaya 16. Date: April 3rd, 6 pm Register free: https://goo.gl/2zc5Nw

292 752

BMSG-GAN https://github.com/akanimax/BMSG-GAN

292 752

Unifying Physics and Deep Learning with TossingBot http://ai.googleblog.com/2019/03/unifying-physics-and-deep-learning-with.html

292 752

How to Evaluate Pixel Scaling Methods for Image Classification With Convolutional Neural Networks https://machinelearningmastery.com/how-to-evaluate-pixel-scaling-methods-for-image-classification/

292 752

Variational inference for Bayesian neural networks https://krasserm.github.io/2019/03/14/bayesian-neural-networks/

292 752

Simulated Policy Learning in Video Models http://ai.googleblog.com/2019/03/simulated-policy-learning-in-video.html

292 752

Deep Learning Explainability: Hints from Physics https://towardsdatascience.com/deep-learning-explainability-hints-from-physics-2f316dc07727

292 752

Machine Learning Mind Map https://www.thelearningmachine.ai/ml

292 752

Deep Learning Examples https://developer.nvidia.com/deep-learning-examples

292 752

6.883 Science of Deep Learning: Bridging Theory and Practice -- Spring 2018 https://people.csail.mit.edu/madry/6.883/

292 752

Stanford CS230: Deep Learning | Autumn 2018 | Lecture 1 - Class Introduction and Logistics https://www.youtube.com/watch?v=PySo_6S4ZAg

292 752

MIT 6.S191: Visualization for Machine Learning (Google Brain) https://www.youtube.com/watch?v=ulLx2iPTIcs

292 752

Программа математики давно пройдена, но пробелы в знаниях все еще тормозят проф.рост? Пройдите обучение на курсе "Математика и статистика для Data Science" и получите возможность уверенно решать нетиповые задачи. Во время обучения вы на примере увидите, как знание математики и статистики работает в решении реальных жизненных задач в области анализа данных, прогнозирования и оптимизации. Забронируйте место на курсе сегодня и получите скидку 20% на обучение → http://bit.ly/2HF8ES0

292 752

How to Load and Manipulate Images for Deep Learning in Python With PIL/Pillow https://machinelearningmastery.com/how-to-load-and-manipulate-images-for-deep-learning-in-python-with-pil-pillow/

292 752

8 Excellent Pretrained Models to get you Started with Natural Language Processing (NLP) https://www.analyticsvidhya.com/blog/2019/03/pretrained-models-get-started-nlp/

292 752

Adaptive - and Cyclical Learning Rates using PyTorch The Learning Rate (LR) is one of the key parameters to tune. Using PyTorch, we’ll check how the common ones hold up against CLR! https://medium.com/@thomas_dehaene/adaptive-and-cyclical-learning-rates-using-pytorch-2bf904d18dee

292 752

Stanford Convolutional Neural Networks for Visual Recognition Course (Review) https://machinelearningmastery.com/stanford-convolutional-neural-networks-for-visual-recognition-course-review/

292 752

Measuring the Limits of Data Parallel Training for Neural Networks http://ai.googleblog.com/2019/03/measuring-limits-of-data-parallel.html

292 752

A Complete Exploratory Data Analysis and Visualization for Text Data https://towardsdatascience.com/a-complete-exploratory-data-analysis-and-visualization-for-text-data-29fb1b96fb6a