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

293 272

Introducing Hypothesis GU Funcs, an Open Source Python Package for Unit Testing https://eng.uber.com/hypothesis-gu-funcs-unit-testing/ Hypothesis General Universal Function Documentation https://hypothesis-gufunc.readthedocs.io/en/latest/

293 272

RecSim: A Configurable Simulation Platform for Recommender Systems https://ai.googleblog.com/2019/11/recsim-configurable-simulation-platform.html article: https://arxiv.org/abs/1909.04847 github: https://github.com/google-research/recsim

293 272

DeepFovea: Using deep learning for foveated reconstruction in AR/VR https://ai.facebook.com/blog/deepfovea-using-deep-learning-for-foveated-reconstruction-in-ar-vr/ code: https://github.com/facebookresearch/DeepFovea full paper: https://research.fb.com/publications/deepfovea-neural-reconstruction-for-foveated-rendering-and-video-compression-using-learned-statistics-of-natural-videos/ @ai_machinelearning_big_data

293 272

What Does Stochastic Mean in Machine Learning? https://machinelearningmastery.com/stochastic-in-machine-learning/

293 272

Machine Learning with Python Cookbook — Chris Albon (en) 2018 @datascienceiot

293 272

Stacked Capsule Autoencoders https://github.com/google-research/google-research/tree/master/stacked_capsule_autoencoders paper : https://arxiv.org/abs/1906.06818 http://akosiorek.github.io/ml/2019/06/23/stacked_capsule_autoencoders.html

293 272

How to Connect Model Input Data With Predictions for Machine Learning https://machinelearningmastery.com/how-to-connect-model-input-data-with-predictions-for-machine-learning/

293 272

Sharing our Experience Upgrading OpenNMT to TensorFlow 2.0 https://blog.tensorflow.org/2019/11/our-experience-upgrading-OpenNMT-to-TensorFlow.html code: https://github.com/OpenNMT/OpenNMT-tf OpenNMT: http://opennmt.net/

293 272

Sberbank's subsidiary Cloud Technologies (provides cloud services under the SberCloud brand) showed the most powerful russian supercomputer Christofari. Power of the supercomputer is 6.67 penaflops (about 6.7 quadrillion operations per second). So Christofari be in the TOP-30 of the world rating.Access will be available for all AI Cloud subscribers. The cost of usage per min on a full power - 5750 RUB (about $90).

293 272

Research Guide: Pruning Techniques for Neural Networks https://heartbeat.fritz.ai/research-guide-pruning-techniques-for-neural-networks-d9b8440ab10d

293 272

Introducing the Next Generation of On-Device Vision Models: MobileNetV3 and MobileNetEdgeTPU http://ai.googleblog.com/2019/11/introducing-next-generation-on-device.html

293 272

Sparse Networks from Scratch: Faster Training without Losing Performance https://arxiv.org/abs/1907.04840 https://timdettmers.com/2019/07/11/sparse-networks-from-scratch/ Sparse Learning Library and Sparse Momentum Resources https://github.com/TimDettmers/sparse_learning

293 272

How to Save a NumPy Array to File for Machine Learning https://machinelearningmastery.com/how-to-save-a-numpy-array-to-file-for-machine-learning/

293 272

Green AI vs Red AI https://arxiv.org/abs/1907.10597 Tackling Climate Change with Machine Learning https://www.reddit.com/r/MachineLearning/comments/da30mv/r_tackling_climate_change_with_machine_learning/

293 272

A Multimodal Language Dataset for Understanding Humor article: https://arxiv.org/pdf/1904.06618.pdf dataset: https://github.com/ROC-HCI/UR-FUNNY

293 272

14 Different Types of Learning in Machine Learning https://machinelearningmastery.com/types-of-learning-in-machine-learning/

293 272

Освойте самую востребованную технологию искусственного интеллекта! Хотите в сжатые сроки получить практические навыки по программированию глубоких нейронных сетей? В SkillFactory в ноябре стартует курс онлайн-курс "Deep Learning и нейронные сети" https://clc.to/_dCORw (при поддержке NVIDIA Corporation). Здесь вы: попробуете свои силы в создании нейронной сети для распознавания рукописных цифр, обучении рекурентной сети задачам прогнозирования временных рядов, разработке нейросетевого чат-бота, создании модели для идентификации лиц и др. Курс основан на практике. Фокус и упор мы делаем не на математическом фундаменте, а именно на понимании задач и практическом применении решений. Узнайте больше о возможностях: https://clc.to/_dCORw

293 272

A Gentle Introduction to Maximum a Posteriori (MAP) for Machine Learning https://machinelearningmastery.com/maximum-a-posteriori-estimation/

293 272

Numerical Computing with Python @datascienceiot

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This project is adapted from the original Dive Into Deep Learning book https://github.com/dsgiitr/d2l-pytorch