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Computer Science and Programming

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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📈 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 140 431 名订阅者,在 技术与应用 类别中位列第 811,并在 意大利 地区排名第 88

📊 受众指标与增长动态

невідомо 创建以来,项目保持高速增长,吸引了 140 431 名订阅者。

根据 01 九月, 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 -772,过去 24 小时变化为 -64,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 8.15%。内容发布后 24 小时内通常能获得 1.97% 的反应,占订阅者总量。
  • 帖子覆盖: 每篇帖子平均可获得 11 442 次浏览,首日通常累积 2 771 次浏览。
  • 互动与反馈: 受众积极参与,单帖平均反应数为 13
  • 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 02 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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帖子存档
Catch up with all the exciting tutorials of International Conference of Machine Learning (ICML'19) here. Indexed with Table o
Catch up with all the exciting tutorials of International Conference of Machine Learning (ICML'19) here. Indexed with Table of Content and powered by VideoKen deep video search

All 1,294 papers at #CVPR2019 in one link. Enjoy woth up-to-date research papers and hot topics of #ArtificialIntelligence #DeepLearning #MachineLearning

A Gentle Introduction to Text Summarization in Machine Learning from FLOYDHUB

https://github.com/lutzroeder/netron Supports: Mac OS, Windows, Linux, Browser version, Python Server

Netron is a viewer for neural network, deep learning and machine learning models.

PyTorch image models, scripts, pretrained weights -- (SE)ResNet/ResNeXT, DPN, EfficientNet, MobileNet-V3/V2/V1, MNASNet, Single-Path NAS, FBNet, and more

Detailed explanation at paper: 👇 https://arxiv.org/pdf/1905.10498.pdf and it's implementation and some results by using pytorch: 👇 https://github.com/facebookresearch/qmnist

MNIST reborn, restored and expanded. Now with an extra 50,000 training samples. If you used the original MNIST test set more than a few times, chances are your models overfit the test set. Time to test them on those extra samples. Now you will use #QMNIST instead of #MNIST

website, which will provide you all up-to-date and necessary information on Artificial Intelligence, Machine Learning, Deep Learning and some brain activities. You will also find TED Talks, Lectures and academic writings on these issues.

Speech2Face: Learning the Face behind a Voice #CVPR2019
Speech2Face: Learning the Face behind a Voice #CVPR2019

Samsung AI's latest work on #GAN to animate realistic head model sequences is setting the internet on fire

Supervisely: end-to-end web-platform for Deep Learning and Computer Vision