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

网络:Computer Science and Programming 意大利87 技术与应用816...

📈 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

8 976

帖子浏览量

~ 2 59524 小时

~ 3 64748 小时

6.29%

参与率

~ 1

每日帖子数

Ads index

beta

帖子存档

142 711

Paper: http://proceedings.mlr.press/v78/dosovitskiy17a/dosovitskiy17a.pdf Code: https://github.com/carla-simulator/carla

142 711

CARLA: An Open Urban Driving Simulator Open-source simulator for autonomous driving

142 711

https://github.com/dsgiitr/graph_nets?fbclid=IwAR39aQbvMVhVxNCbQOoxkxdkYgnfd3tMuqzvWBqz048i8iE8Sjavcqbh56M

142 711

PyTorch Implementation and Explanation of Graph Representation Learning papers involving DeepWalk, GCN, GraphSAGE, ChebNet & GAT.

142 711

Paper link, Blog and code are at their Github page: https://xbpeng.github.io/projects/DeepMimic/index.html?fbclid=IwAR3SaHAM-nii9UcpBJsWH-w7swuJudV9ouC7Ige8iuZDF0lbBy4ThchKhTo

142 711

DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills

142 711

Article: https://www.microsoft.com/en-us/research/blog/zero-deepspeed-new-system-optimizations-enable-training-models-with-over-100-billion-parameters/ Github: https://github.com/microsoft/DeepSpeed

142 711

ZeRO & DeepSpeed: New system optimizations enable training models with over 100 billion parameters

142 711

http://introtodeeplearning.com/

142 711

Course from MIT 6.S191 "Introduction to Deep Learning". Methods and applications in game play, medicine, language, art, computer vision, robotics and more

142 711

This video is synthetic and was created using deep learning

142 711

https://ai.facebook.com/blog/-introducing-pytorch3d-an-open-source-library-for-3d-deep-learning/

142 711

Introducing PyTorch3D: An open-source library for 3D deep learning. PyTorch3D: Faster, flexible 3D deep learning research

142 711

https://towardsdatascience.com/end-to-end-machine-learning-from-data-collection-to-deployment-ce74f51ca203 If you want to play with the app and see how it looks like: 💻Demo: https://www.reviews.ai2prod.com/ Code: https://github.com/MarwanDebbiche/post-tuto-deployment

142 711

End to End Machine Learning: From Data Collection to Deployment. - Collect and scrape data with Scrapy / Selenium - Train a deep character CNN for (English) sentiment analysis using PyTorch - Build an interactive web app with Dash to serve the model in real-time - Put everything in Docker Compose - Deploy to AWS on a custom domain name

142 711

More than 200 NLP datasets - this is gold (last update 21.01.202) https://quantumstat.com/dataset/dataset.html and also Google provided dataset search tool for publicly available datasets: https://datasetsearch.research.google.com/

142 711

Paper: https://arxiv.org/pdf/2001.05613.pdf Project page: http://www.ynl.t.u-tokyo.ac.jp/research/vmocap-syn/ Dataset will be available publicly soon

142 711

Synergetic Reconstruction from 2D Pose and 3D Motion for Wide-Space Multi-Person Video Motion Capture in the Wild

142 711

Paper pdf: https://arxiv.org/pdf/2001.05201.pdf Github: https://wywu.github.io/projects/EBT/EBT.html Youtube: https://youtu.be/tNPuAnvijQk

142 711

Everybody’s Talkin’: Let Me Talk as You Want This paper presents a method to edit a target portrait footage by taking a sequence of audio as input to synthesize a photo-realistic video.