现已上线！2025 年 Telegram 研究 — 年度关键洞察 
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
显示更多📈 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
帖子存档
292 713
DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learning
https://www.nature.com/articles/s41598-018-38343-3
292 713
Convolutional Neural Nets in PyTorch
https://blog.algorithmia.com/convolutional-neural-nets-in-pytorch/
292 713
GANimation: Anatomically-aware Facial Animation from a Single Image
https://github.com/albertpumarola/GANimation
292 713
Introducing PlaNet: A Deep Planning Network for Reinforcement Learning
https://ai.googleblog.com/2019/02/introducing-planet-deep-planning.html
292 713
How Should Self-Driving Cars Choose Who Not to Kill?
https://medium.com/@MORGANMEAKER/how-should-self-driving-cars-choose-who-not-to-kill-442f2a5a1b59?source=topic_page---------------------------20
292 713
Image Segmentation using Python’s scikit-image module
https://towardsdatascience.com/image-segmentation-using-pythons-scikit-image-module-533a61ecc980
292 713
Box Convolution Layer for ConvNets
This is a PyTorch implementation of the box convolution layer as introduced in the 2018 NeurIPS paper:
https://github.com/shrubb/box-convolutions
292 713
What To Optimize for? Loss Function Cheat Sheet
https://medium.com/@urimerhav/what-to-optimize-for-loss-function-cheat-sheet-5fc8b1339939
292 713
Introduction to gradient boosting on decision trees with Catboost
Today I would like to share my experience with open source machine learning library, based on gradient boosting on decision trees, developed by Russian search engine company — Yandex.
https://towardsdatascience.com/introduction-to-gradient-boosting-on-decision-trees-with-catboost-d511a9ccbd14
292 713
The Ancient Secrets of Computer Vision University of Washington.
Free course
This class is a general introduction to computer vision. It covers standard techniques in image processing like filtering, edge detection, stereo, flow, etc. , as well as newer, machine-learning based computer vision.
https://pjreddie.com/courses/computer-vision/
292 713
Predict malignancy in cancer tumors with your own neural network
https://towardsdatascience.com/predict-malignancy-in-breast-cancer-tumors-with-your-own-neural-network-and-the-wisconsin-dataset-76271a05e941?source=collection_home---4------0---------------------
292 713
A Comprehensive Introduction to Different Types of Convolutions in Deep Learning
https://towardsdatascience.com/a-comprehensive-introduction-to-different-types-of-convolutions-in-deep-learning-669281e58215
292 713
Reflections on the State of AI: 2018
https://medium.com/evolution-one/reflections-on-the-state-of-ai-2018-46504e25263d?source=topic_page---------4------------------1
