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 24 499 名订阅者，在 教育 类别中位列第 8 053，并在 伊朗 地区排名第 13 774 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 24 499 名订阅者。
根据 30 六月, 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 -131，过去 24 小时变化为 -4，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 7.24%。内容发布后 24 小时内通常能获得 1.98% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 1 773 次浏览，首日通常累积 484 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 01 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。
24 499
订阅者
-424 小时
-187 天
-13130 天
帖子存档
The fashion industry is on the verge of an unprecedented change. The implementation of machine learning, computer vision, and artificial intelligence (AI) in fashion applications is opening lots of new opportunities for this industry. This paper provides a comprehensive survey on this matter, categorizing more than 580 related articles into 22 well-defined fashion-related tasks. Such structured task-based multi-label classification of fashion research articles provides researchers with explicit research directions and facilitates their access to the related studies, improving the visibility of studies simultaneously. For each task, a time chart is provided to analyze the progress through the years. Furthermore, we provide a list of 86 public fashion datasets accompanied by a list of suggested applications and additional information for each.
link: https://arxiv.org/abs/2111.00905
@Machine_learn
🔊 Torchaudio: an audio library for PyTorch
Github: https://github.com/pytorch/audio
Paper: https://arxiv.org/abs/2110.15018v1
Dataset: https://paperswithcode.com/dataset/ljspeech
@Machine_learn
تفخیف 50% برای دوستان عزیز با زمان محدود. جهت خرید به ایدی بنده مراجعه کنین @Raminmousa
GoEmotions: A Dataset for Fine-Grained Emotion Classification
http://ai.googleblog.com/2021/10/goemotions-dataset-for-fine-grained.html
@Machine_learn
Recurrent Neural Networks for Edge Intelligence: A Survey #Survey #RNN @Machine_learn
Survey on Recurrent Neural Network in Natural Language Processing #Survey #RNN @Machine_learn
Time Series Data Imputation: A Survey on Deep Learning Approaches #RNN #Survey @Machine_learn
A Critical Review of Recurrent Neural Networks
for Sequence Learning #Survey #RNN @Machine_learn
Recurrent Neural Network
TINGWU WANG,
MACHINE LEARNING GROUP,
UNIVERSITY OF TORONTO #Slide #RNN @Machine_learn
Computational Tutorial:
An introduction to LSTMs in Tensorflow #Slide #RNN @Machine_learn
Introduction to RNNs!
Arun Mallya! #RNN #Slide @Machine_learn
AlphaRotate: A Rotation Detection Benchmark using TensorFlow
Github: https://github.com/yangxue0827/RotationDetection
Paper: https://arxiv.org/abs/2109.11906v1
Dataset: https://paperswithcode.com/dataset/dota
Documentation: https://rotationdetection.readthedocs.io/en/latest/
@Machine_learn
Unified Data Collection for Visual-Inertial Calibration via Deep Reinforcement Learning
Github: https://github.com/ethz-asl/Learn-to-Calibrate
Paper: https://arxiv.org/abs/2109.14974v1
@Machine_learn
TensorFlow Model Optimization Toolkit — Collaborative Optimization API
https://blog.tensorflow.org/2021/10/Collaborative-Optimizations.html
@Machine_learn
Transfer Learning for
Natural Language
Processing #Book @Machine_learn
Discover the world of Machine Learning using Python
algorithm analysis, ide and libraries. Projects focused on
beginners. #Book @Machine_learn
Distributed Artificial Intelligence
A Modern Approach
Edited by
Satya Prakash Yadav, Dharmendra Prasad Mahato, and Nguyen Thi Dieu Linh #Book @Machine_learn
