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 24 508 名订阅者，在 教育 类别中位列第 8 019，并在 伊朗 地区排名第 13 748 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 24 508 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 -101，过去 24 小时变化为 3，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 6.50%。内容发布后 24 小时内通常能获得 2.21% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 1 594 次浏览，首日通常累积 541 次浏览。
- 互动与反馈： 受众积极参与，单帖平均反应数为 2。
- 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 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 教育 类别中的关键影响点。
24 508
订阅者
+324 小时
-97 天
-10130 天
帖子存档
#Chest Radiograph
Pathology Categorization
via Transfer Learning #Chapter13 @Machine_learn
#Scalable High
Performance Image
Registration Framework
by Unsupervised Deep
Feature Representations
Learning
#Chapter11 @Machine_learn
#Deformable MR Prostate
Segmentation via Deep
Feature Learning and Sparse
Patch Matching
#Chapter9 @Machine_learn
#Deep Learning Tissue
Segmentation in Cardiac
Histopathology Images #Chapter8 @Machine_learn
#Deep Voting and Structured
Regression for Microscopy
Image Analysis
#Chapter7 @Machine_learn
#Deep Cascaded Networks for
Sparsely Distributed Object
Detection from Medical
Images
#Chapter6 @Machine_learn
#Automatic Interpretation of
Carotid Intima–Media
Thickness Videos Using
Convolutional Neural
Networks
#Chapter5 @Machine_learn
#Multi-Instance Multi-Stage
Deep Learning for Medical
Image Recognition
#Chapter4 @Machine_learn
#An Introduction to Deep
Convolutional Neural Nets for
Computer Vision #Chapter2 @Machine_learn
#An Introduction to Neural
Networks and Deep Learning #Chapter1 @Machine_learn
#learning predictive analytics with python
#book
#Machine_learn
#Datascience #MachineLearning #Artificialintelligence #Statistics
p.y.b:
Here is a list of what I believe are the 10 Practical Steps for #DataScience:
1. Programming
a. Python - https://lnkd.in/gGQ7cuv
b. R - https://lnkd.in/giMGbph
c. SQL - https://lnkd.in/gM8nMNP
d. Command Line - https://lnkd.in/e3EQuis
2. Stats/Prob/Math
a. Coursera's Statistics w/ R - https://lnkd.in/gGT9NEf
b. edX's Probability - https://lnkd.in/gpUyC3P
c. Khan Academy Linear Algebra - https://lnkd.in/gMshbX4
3. Data Viz
a. Python Matplotlib- https://lnkd.in/gr3ifNt
b. R ggplot2 - https://lnkd.in/eThJXNr
4. Data Manipulation
a. Python Pandas - https://lnkd.in/g9kfpX4
b. R dplyr - https://lnkd.in/gAWusih
5. #MachineLearning
a. Google Crash Course - https://lnkd.in/gSgkVcT
b. Stanford Coursera - https://lnkd.in/g8ZG557
c. ISLR Book - https://lnkd.in/gk8GPZC
6. Experimental Design
a. Udacity A/B Testing - https://lnkd.in/gCerh4f
7. Business Sense
a. Metrics - https://lnkd.in/gZAG7bS
8. Communication
a. Storytelling - https://lnkd.in/gwjxVUu
9. Profile Building
a. GitHub - https://lnkd.in/g4r9naJ
b. LinkedIn - https://lnkd.in/g-KHHEC
c. Kaggle - https://lnkd.in/gBC77Hu
d. DS Resume - https://lnkd.in/gU8WVAF
🏅 10. Job Search
a. Daily Expert Tips & Advice - https://lnkd.in/g8z-xXD
---
Hope this helps! 👍
Updated on my site - http://www.claoudml.co/
#Reinforcement Learning Textbook - Sutton
#book
@Machine_learn
سلام
از دوستان اگر کسی پایان نامش مرتبط با موضوع«بهبود استخراج قوانین انجمني با استفاده از روش های تکاملي» هستش لطفا جهت همکاری به این ایدی پیام بدن. با تشکر
@mahdi7_7_7
#LARGE SCALE GAN TRAINING FOR HIGH FIDELITY NATURAL IMAGE SYNTHESIS - ICLR 2019
@Machine_learn
