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 142 711 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 142 711 名订阅者。

根据 15 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 -1 289，过去 24 小时变化为 -46，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 6.44%。内容发布后 24 小时内通常能获得 1.85% 的反应，占订阅者总量。
帖子覆盖： 每篇帖子平均可获得 9 197 次浏览，首日通常累积 2 646 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 16 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。

142 711

订阅者

-4624 小时

-2077 天

-1 28930 天

9 197

帖子浏览量

~ 2 64624 小时

~ 3 57748 小时

6.44%

参与率

~ 1

每日帖子数

Ads index

beta

帖子存档

142 711

The Power and Limitations of Deep Learning with Yann LeCun

142 711

https://www.endtoend.ai/blog/neurips2019-rl/?fbclid=IwAR09NZQzuLPpWsEuwiqylrwmNXd64Imyj1v8TQWWYbCiojcKli3ae8lhle8

142 711

All list of accepted REINFORECEMENT LEARNING papers to NeurIPS 2019

142 711

https://medium.com/@souravboss.bose/comprehensive-topic-modelling-with-nmf-lsa-plsa-lda-lda2vec-part-1-20002a8e03ae

142 711

How Stuff Works: A Comprehensive Topic Modelling Guide with NMF, LSA, PLSA, LDA & lda2vec (Part-1). Medium article from Sourav Bose

142 711

This link is not valid curently. Alternatively, we can use this link: http://openaccess.thecvf.com/CVPR2019.py

142 711

A practical approach to learning machine learning GitHub : https://github.com/GokuMohandas/practicalAI - 📚 Notebooks on topics from basic Python to advanced deep learning techniques #PyTorch - 🖥 Run everything using #Colab : https://colab.research.google.com/…/GokuMohand…/practicalAI/

142 711

Paper link: https://arxiv.org/pdf/1905.05172.pdf Official Page: https://shunsukesaito.github.io/PIFu/ Code status: coming soon

142 711

From ICCV 19: PIFu, an end-to-end deep learning method that can reconstruct a 3D model of a person wearing clothes from a single image.

142 711

Let's have a little fun. World of Machine Learning, Deep Learning, Python with frameworks

142 711

Github repo: https://github.com/vietnguyen91/Super-mario-bros-A3C-pytorch?fbclid=IwAR2H1BMV24t2d2VFr4QhxIEJ21jpgtTN7ev2IlAe_ledTM3msuBzhph4P2Q Youtube demo: https://www.youtube.com/watch?v=YdKz1eBv1kc&feature=youtu.be&fbclid=IwAR1eXCi43R9cwS7Kk5s49Aq0VOXJik-lzFxyL_JgyuaC9jTnkCIqOdkHV14

142 711

Let your machine play Super Mario Bros! and remind our youth. Here is python implementation of Asynchronous Advantage Actor-Critic (A3C) algorithm for Super Mario Bros.

142 711

https://www.viva64.com/en/b/0660/

142 711

PVS-Studio Analyzer. Tool for detecting bugs and security weaknesses in the source code of programs, written in C, C++, C# and Java. Download, try and make a clean code, which less bugs

142 711

https://www.youtube.com/watch?v=7lwJJgag0lM&feature=youtu.be&fbclid=IwAR2SA8ffh1nfpy8FmWIoHruq5LVVpWs5Ov7MFyEPuYHeJcAHFq9R2QMHwRA

142 711

How to drive #Weights and #Biases matrices in #Neural_Networks for #Machine_Learning. Code available at: https://github.com/yasser64b/Machine-Learning-

142 711

Easily install with pip and try in your code https://github.com/alexmojaki/heartrate

142 711

Heartrate - real-time visualization of code execution. Observe your Python code with this tool. Python 3.5+

142 711

https://search.videoken.com/?orgId=198&fbclid=IwAR3wOa4qRcRZ2FlzQKx7o4gVSD7oX498qh5hbQJZNAFOlQ8n3PzeIj0WH6E

142 711

Centre for Computational Statistics and Machine Learning from UCL's Machine Learning Summer School (MLSS'19) video lectures The topics range from optimization and Bayesian inference to deep learning, reinforcement learning, and Gaussian processes. The lectures are of tutorial style, starts from basics, but then quickly picking up the pace so that after 2-4 hours of teaching, they arrive at the state of the art in the subject area.