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

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网络:Computer Science and Programming 意大利88 技术与应用804...

📈 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 140 441 名订阅者，在 技术与应用 类别中位列第 804，并在 意大利 地区排名第 88 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 140 441 名订阅者。

根据 31 八月, 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 -734，过去 24 小时变化为 -18，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 7.96%。内容发布后 24 小时内通常能获得 1.94% 的反应，占订阅者总量。
帖子覆盖： 每篇帖子平均可获得 11 177 次浏览，首日通常累积 2 728 次浏览。
互动与反馈： 受众积极参与，单帖平均反应数为 14。
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 01 九月, 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。

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140 441

订阅者

-1824 小时

-2617 天

-73430 天

11 177

帖子浏览量

~ 2 72824 小时

~ 3 75748 小时

7.96%

参与率

无数据

每日帖子数

Ads index

beta

帖子存档

140 427

NeurIPS 2020 highlights at: https://crossminds.ai/category/neurips%202020/ Also follow recent conferences and discover your inspirations and see what's happening

140 427

NeurIPS 2020 is the largest machine learning conference and is just around the corner! Check out this exclusive collection of 100+ pre-recorded talks on the latest machine learning research from world-renowned tech companies and universities. Videos are shared by the authors with free public access

140 427

Project page: https://video-nerf.github.io/ Paper: https://arxiv.org/pdf/2011.12950.pdf Code: coming soon

140 427

3D photo is great, but have you ever seen 3D *free-viewpoint video*? Space-time neural rendering from a single, casually captured video! https://youtu.be/2tN8ghNu2sI

140 427

https://laconicml.com/coursera-artificial-intelligence-courses/?fbclid=IwAR2UlS5rr5ya9f3YtxQFdyQnA69ll-m3xTN5kdQVaiUpRp5nkaIyOuq5e8g Yann LeCun’s Deep Learning Course at CDS also free now: https://cds.nyu.edu/deep-learning/

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Top 40 COMPLETELY FREE Coursera Artificial Intelligence and Computer Science Courses

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https://medium.com/the-innovation/130-machine-learning-projects-solved-and-explained-605d188fb392

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Top deep learning Github repositories https://github.com/mbadry1/Top-Deep-Learning

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List of top 200 deep learning Github repositories sorted by the number of stars.

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Rotated Binary Neural Network Github (Pytorch implementation): https://github.com/lmbxmu/RBNN Paper: https://arxiv.org/abs/2009.13055

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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)

140 427

https://github.com/brinesoftwares/Flutter-AI-Rubik-cube-Solver?fbclid=IwAR0agon7_lmFDOoBeT3ru9skWvTer3syzLFz_vHDZy9isC3FzWTJJ3JufsE

140 427

AI based Rubik's Cube Solver using Flutter and Python

140 427

https://www.nature.com/articles/s41586-020-2649-2

140 427

NumPy provides an easily readable, expressive, high-level API for array programming. It takes care of the underlying mechanics that make operations fast.

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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

140 427

Here is a new, and free book on Machine Learning from scratch. It includes the math and code examples. Solid reference.

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Organize the daily influx of ML content in meaningful ways without feeling overwhelmed, By Goku Mohandas et al. : https://madewithml.com/collections/

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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