现已上线！2025 年 Telegram 研究 — 年度关键洞察 
Machine learning books and papers
前往频道在 Telegram
📈 Telegram 频道 Machine learning books and papers 的分析概览
频道 Machine learning books and papers (@machine_learn) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 24 505 名订阅者，在 教育 类别中位列第 8 033，并在 伊朗 地区排名第 13 749 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 24 505 名订阅者。
根据 03 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 -99，过去 24 小时变化为 2，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 6.54%。内容发布后 24 小时内通常能获得 2.24% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 1 603 次浏览，首日通常累积 549 次浏览。
- 互动与反馈： 受众积极参与，单帖平均反应数为 1。
- 主题关注点： 内容集中在 disorder, psy, مقاله, framework, graph 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：
“Admin: @Raminmousa
ID: @Machine_learn
link: https://t.me/Machine_learn”
凭借高频更新（最新数据采集于 04 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 教育 类别中的关键影响点。
24 505
订阅者
+224 小时
-107 天
-9930 天
帖子存档
Machine Learning for OpenCV
A practical introduction to the world of machine learning and
image processing using #OpenCV and #Python #book #ML
@Machine_learn
Machine Learning for OpenCV
A practical introduction to the world of machine learning and
image processing using #OpenCV and #Python #book #ML
@Machine_learn
Machine Learning Refined
Foundations, Algorithms, and Applications
JEREMY WATT, REZA BORHANI, AND AGGELOS K. KATSAGGELOS
#book #ML
@Machine_learn
Machine Learning Refined
Foundations, Algorithms, and Applications
JEREMY WATT, REZA BORHANI, AND AGGELOS K. KATSAGGELOS
#book #ML
@Machine_learn
@Machine_learn
New paper on training with pseudo-labels for semantic segmentation
Semi-Supervised Segmentation of Salt Bodies in Seismic Images:
SOTA (1st place) at TGS Salt Identification Challenge.
Github: https://github.com/ybabakhin/kaggle_salt_bes_phalanx
ArXiV: https://arxiv.org/abs/1904.04445
#GCPR2019 #Segmentation #CV
Learning Scrapy Learn the art of efficient web scraping and crawling with Python
#book #python #Scrapy
@Machine_leaen
Learning Scrapy Learn the art of efficient web scraping and crawling with Python
#book #python #Scrapy
@Machine_leaen
ensemble-machine-learning@netWorkArtificial
#book
@Machine_learn
@Machine_learn
Wasserstein Robust Reinforcement Learning
article:https://arxiv.org/abs/1907.13196v1
pdf: https://arxiv.org/pdf/1907.13196v1.pdf
@Machine_learn
#CapsuleNet #code
Stacked Capsule Autoencoders
http://akosiorek.github.io/ml/2019/06/23/stacked_capsule_autoencoders.htm
@Machine_learn
#code #paper
Y-Autoencoders: disentangling latent representations via sequential-encoding
Article: https://arxiv.org/abs/1907.10949
GitHub: https://github.com/mpatacchiola/Y-AE
@Machine_learn #code #paper
FixRes is a simple method for fixing the train-test resolution discrepancy. It can improve the performance of any convolutional neural network architecture.
Github: https://github.com/facebookresearch/FixRes
Article:https://arxiv.org/abs/1906.06423
@Machine_learn #code #paper
FixRes is a simple method for fixing the train-test resolution discrepancy. It can improve the performance of any convolutional neural network architecture.
Github: https://github.com/facebookresearch/FixRes
Article:https://arxiv.org/abs/1906.06423
Simple Deep Learning for
Programmers Write your own modern neural networks in Keras and Python for images and sequence data
#By: The Lazy Programmer
#book #DL
@Machine_learn
Simple Deep Learning for
Programmers Write your own modern neural networks in Keras and Python for images and sequence data
#By: The Lazy Programmer
#book #DL
@Machine_learn
@Machine_learn
__________________________
How to Develop an Information Maximizing GAN (InfoGAN) in Keras
https://machinelearningmastery.com/how-to-develop-an-information-maximizing-generative-adversarial-network-infogan-in-keras/
Sentiment Analysis by Capsules∗
#paper #DL #SA
@Machine_learn
Sentiment Analysis by Capsules∗
#paper #DL #SA
@Machine_learn
