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 24 502 名订阅者，在 教育 类别中位列第 8 036，并在 伊朗 地区排名第 13 785 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 24 502 名订阅者。
根据 01 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 -127，过去 24 小时变化为 -5，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 7.47%。内容发布后 24 小时内通常能获得 2.04% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 1 829 次浏览，首日通常累积 500 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 02 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。
24 502
订阅者
-524 小时
-207 天
-12730 天
帖子存档
Machine Learning for Computer Architecture
http://ai.googleblog.com/2021/02/machine-learning-for-computer.html
@Machine_learn
WeNet open source, production first and production ready end-to-end (E2E) speech recognition toolkit
Github: https://github.com/mobvoi/wenet
Paper: https://arxiv.org/abs/2102.01547v1
Tutorial: https://github.com/mobvoi/wenet/blob/main/docs/tutorial.md
@Machine_learn
Open Datasets for Research
During last week there were several news about newly open datasets for researchers.
1. Twitter opened “full history of public conversation” for academics (specifically, for academics):
https://www.theverge.com/2021/1/26/22250203/twitter-academic-research-public-tweet-archive-free-access
We can happily conduct researches about social networks graphs, users behavior and fake news (especially fake news🙃) without fighting with Twitter API.
2. Papers with code are now also Papers with Datasets:
https://www.paperswithcode.com/datasets
Not for only NLP, but for all fields structured for easy search and download.
@Machine_learn
Feature Engineering for Machine Learning
Principles and Techniques for Data Scientists
#book
@Machine_learn
Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODE #paper @Machine_learn
Evaluating Design Trade-offs in Visual Model-Based Reinforcement Learning
http://ai.googleblog.com/2021/02/evaluating-design-trade-offs-in-visual.html
@Machin_learn
Improving Mobile App Accessibility with Icon Detection
http://ai.googleblog.com/2021/01/improving-mobile-app-accessibility-with.html
@Machine_learn
TIN: Transferable Interactiveness Network
Github: https://github.com/DirtyHarryLYL/Transferable-Interactiveness-Network
HOI-Learning-List: https://github.com/DirtyHarryLYL/HOI-Learning-List
Paper: https://arxiv.org/abs/2101.10292v1
@Machine_learn
🔸لیستی از برترین کانالهای آموزشی در زمینه های هوشمصنوعی, پایتون و یادگیری ماشین
❯ هوش مصنوعی:
1️⃣ @Ai_Tv
2⃣ @HomeAI
❯ یادگیری ماشین و یادگیری عمیق :
1️⃣ @Machine_learn
2⃣ @cvision
❯ علم داده:
1⃣ @mr_ie
❯ آموزش پایتون و برنامه نویسی :
1⃣ @pythony
2⃣ @pythonchallenge
3⃣ @Programming4all_0to100
Stabilizing Live Speech Translation in Google Translate
http://ai.googleblog.com/2021/01/stabilizing-live-speech-translation-in.html
@Machine_learn
Self-Adaptive Training
Github: https://github.com/LayneH/self-adaptive-training
Paper: https://arxiv.org/abs/2101.08732v1
@Machine_learn
سلام از دوستان كسي هست كه به #رايانش_تكاملي مسلط باشه ممنون ميشم بهم پيام بده
@Raminmousa
A Visual Intro to NumPy and Data Representation
.
Link : https://jalammar.github.io/visual-numpy/
@Machine_learn
👉Lecture Notes for Linear Algebra Featuring Python
.
GitHub link : https://github.com/MacroAnalyst/Linear_Algebra_With_Python
@Machine_learn
🔥 Fast convolutional neural networks on FPGAs with hls4ml
Github: https://github.com/fastmachinelearning/hls4ml
Paper: https://arxiv.org/abs/2101.05108v1
Documentation: https://fastmachinelearning.org/hls4ml/
@Machine_learn
Alex_Thomas_Natural_Language_Processing_with_Spark_NLP_Learn.pdf
#book #NLP
@Machine_learn
Superpixel-based Refinement for Object Proposal Generation
Github: https://github.com/chwilms/superpixelRefinement
Paper: https://arxiv.org/abs/2101.04574v1
@Machine_learn
Gender recognition in the wild: a robustness evaluation over corrupted images
Github: https://github.com/MiviaLab/GenderRecognitionFramework
Paper: https://link.springer.com/article/10.1007/s12652-020-02750-0
@Machine_lear
