现已上线！2025 年 Telegram 研究 — 年度关键洞察 
Computer Science and Programming
前往频道在 Telegram
Channel specialized for advanced topics of: * Artificial intelligence, * Machine Learning, * Deep Learning, * Computer Vision, * Data Science * Python Admin: @otchebuch Memes: @memes_programming Ads: @Source_Ads, https://telega.io/c/computer_science
显示更多📈 Telegram 频道 Computer Science and Programming 的分析概览
频道 Computer Science and Programming (@computer_science_and_programming) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 142 737 名订阅者，在 技术与应用 类别中位列第 816，并在 意大利 地区排名第 87 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 142 737 名订阅者。
根据 14 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 -1 292，过去 24 小时变化为 -44，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 6.29%。内容发布后 24 小时内通常能获得 1.82% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 8 976 次浏览，首日通常累积 2 595 次浏览。
- 互动与反馈： 受众积极参与，单帖平均反应数为 17。
- 主题关注点： 内容集中在 sellerflash, github, developer, pricing, waybienad 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：
“Channel specialized for advanced topics of:
* Artificial intelligence,
* Machine Learning,
* Deep Learning,
* Computer Vision,
* Data Science
* Python
Admin: @otchebuch
Memes: @memes_programming
Ads: @Source_Ads,
https://telega.io/c/computer_sc...”
凭借高频更新（最新数据采集于 15 六月, 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。
142 737
订阅者
-4424 小时
-2007 天
-1 29230 天
帖子存档
List of top 200 deep learning Github repositories sorted by the number of stars.
Rotated Binary Neural Network
Github (Pytorch implementation): https://github.com/lmbxmu/RBNN
Paper: https://arxiv.org/abs/2009.13055
Binary Neural Network (BNN) is best feet for reducing the complexity of deep neural networks. But, it suffers severe performance degradation. Rotation based training leads to around 50% weight flips which maximize the information gain and showed state-of-the-arts in benchmark datasets
Rotated Binary Neural Network (RBNN)
NumPy provides an easily readable, expressive, high-level API for array programming. It takes care of the underlying mechanics that make operations fast.
https://dafriedman97.github.io/mlbook/content/table_of_contents.html
And The list of Most Updated and Free Artificial Intelligence, Machine Learning, Data Science, Deep Learning, Mathematics, Python Programming Resources. (Last Update: Sept 9, 2020):
https://www.marktechpost.com/free-resources/?fbclid=IwAR0hc2qkxPMXhQGzsg07ffgFecRr01tSCRqlhb_XMR6PjPt1KNdy68cLy9w
Here is a new, and free book on Machine Learning from scratch. It includes the math and code examples. Solid reference.
Organize the daily influx of ML content in meaningful ways without feeling overwhelmed,
By Goku Mohandas et al. :
https://madewithml.com/collections/
Brian Huge, Antoine Savine :
https://arxiv.org/abs/2005.02347
TF1 implementation notebook on Colab: https://colab.research.google.com/github/differential-machine-learning/notebooks/blob/master/DifferentialML.ipynb
FREE PDF download:
https://d2l.ai/d2l-en.pdf
Download the book in 'notebook' format to read and execute locally:
https://d2l.ai/chapter_installation/index.html
Website:
https://d2l.ai
Github:
https://github.com/d2l-ai/d2l-en
Article:
https://www.amazon.science/latest-news/amazon-scientists-author-popular-deep-learning-book
Dive Into Deep Learning
August 2020 and FREE version!!! D2L is the 987-page book that Amazon scientists have compiled over the past two years and has finally been completed... an interactive and ' open source book ' with code, math and discussions.
What makes this book unique is that it was created with Jupyter Notebook and with the idea of ′′ Learning with Practice "... that is, the book in its entirety consists of executable code with adaptations in PyTorch, TensorFlow and MXNet.
Classification library
https://github.com/Tessellate-Imaging/monk_v1
Notebooks - https://github.com/Tessellate-Imaging/monk_v1/tree/master/study_roadmaps/4_image_classification_zoo
Detection and Segmentation Library
https://github.com/Tessellate-Imaging/
Monk_Object_Detection
Notebooks: https://github.com/Tessellate-Imaging/Monk_Object_Detection/tree/master/application_model_zoo
80+ Jupyter Notebook tutorials on image classification, object detection and image segmentation in various domains
📌 Agriculture and Food
📌 Medical and Healthcare
📌 Satellite
📌 Security and Surveillance
📌 ADAS and Self Driving Cars
📌 Retail and E-Commerce
📌 Wildlife
Baidu publishes PP-YOLO and pushes the state of the art in object detection research.
Tackled the problem of defining a perturbation set for real-world perturbations which cannot be easily described with a set of equations.
Paper: https://arxiv.org/abs/2007.08450
Blog post: https://locuslab.github.io/2020-07-20-perturbation/
Code: https://github.com/locuslab/perturbation_learning
