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Artificial Intelligence && Deep Learning

Artificial Intelligence && Deep Learning

前往频道在 Telegram

Channel for who have a passion for - * Artificial Intelligence * Machine Learning * Deep Learning * Data Science * Computer vision * Image Processing * Research Papers With advertising offers contact:

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印度6 003技术与应用2 289

📈 Telegram 频道 Artificial Intelligence && Deep Learning 的分析概览

频道 Artificial Intelligence && Deep Learning (@deeplearning_ai) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 58 023 名订阅者，在 技术与应用 类别中位列第 2 289，并在 印度 地区排名第 6 003 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 58 023 名订阅者。

根据 24 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 -193，过去 24 小时变化为 17，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 9.42%。内容发布后 24 小时内通常能获得 N/A% 的反应，占订阅者总量。
  • 帖子覆盖： 每篇帖子平均可获得 5 467 次浏览，首日通常累积 0 次浏览。
  • 互动与反馈： 受众积极参与，单帖平均反应数为 16。
  • 主题关注点： 内容集中在 github, learning, estimation, dataset, engineer 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 for who have a passion for - * Artificial Intelligence * Machine Learning * Deep Learning * Data Science * Computer vision * Image Processing * Research Papers With advertising offers contact:”

凭借高频更新（最新数据采集于 25 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。

58 023
订阅者
+1724 小时
-237 天
-19330 天
帖子存档
Artificial Intelligence && Deep Learning
Artificial Intelligence && Deep Learning
58 023
State of the art in Video Object Segmentation. [paper]: https://www.catalyzex.com/paper/arxiv:2106.05210 Free extension to get code for ML papers (❤️'d by Andrew Ng): Chrome: AI/ML Papers with Code Everywhere - CatalyzeX https://t.me/DeepLearning_ai

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BIG STEP FROM CHINA! GPT-3 Scared You? Meet Wu Dao 2.0: A Monster of 1.75 Trillion Parameters Wu Dao 2.0 is 10x larger than GPT-3. Imagine what it can do. https://towardsdatascience.com/gpt-3-scared-you-meet-wu-dao-2-0-a-monster-of-1-75-trillion-parameters-832cd83db484 join us: https://t.me/DeepLearning_ai

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Millions of learners have used Class Central to find their next course. https://www.classcentral.com/ Join us and stay up to
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Artificial Intelligence && Deep Learning
Artificial Intelligence && Deep Learning
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CS224W: Machine Learning with Graphs - Stanford / Winter 2021 https://www.youtube.com/playlist?list=PLuv1FSpHurUemjLiP4L1x9k6Z9D8rNbYW Full Stack Deep Learning - Spring 2021 - UC Berkeley https://www.youtube.com/playlist?list=PLuv1FSpHurUc2nlabZjCLLe8EQa9fOoa9 Introduction to Deep Learning (I2DL) - Technical University of Munich https://www.youtube.com/playlist?list=PLuv1FSpHurUdmk7v06MDyIx0SDxTrIoqk 3D Computer Vision - National University of Singapore - 2021 https://www.youtube.com/playlist?list=PLuv1FSpHurUflLnJF6hgi0FkeNG1zSFCZ CV3DST - Computer Vision 3: Detection, Segmentation and Tracking https://www.youtube.com/playlist?list=PLuv1FSpHurUd08wNo1FMd3eCUZXm8qexe ADL4CV - Advanced Deep Learning for Computer Vision https://www.youtube.com/playlist?list=PLuv1FSpHurUcQi2CwFIVQelSFCzxphJqz join us: https://t.me/DeepLearning_ai

Artificial Intelligence && Deep Learning
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2021- Courses List of Machine Learning, Deep Learning, and Computer Vision from a top school
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Artificial Intelligence && Deep Learning
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Review — SFA: Simplified-Fast-AlexNet (Blur Classification) In this story, Blur Image Classification based on Deep Learning, (SFA), is reviewed. In this paper: Simplified-Fast-AlexNet (SFA) is designed to classify if an image is blurred by defocus blur, Gaussian blur, haze blur, or motion blur. https://medium.com/nerd-for-tech/review-sfa-simplified-fast-alexnet-blur-classification-4121e6d813f9 👉https://t.me/DeepLearning_ai

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500 + 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗟𝗶𝘀𝘁 𝘄𝗶𝘁𝗵 𝗰𝗼𝗱𝗲 https://github.com/ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code 👉https://t.me/DeepLearning_ai

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PAPER WITH CODES https://paperswithcode.com/ The mission of Papers with Code is to create a free and open resource with Machine Learning papers, code and evaluation tables. We believe this is best done together with the community, supported by NLP and ML. Also operate specialized portals for papers with code in astronomy, physics, computer sciences, mathematics and statistics. #Contributing Anyone can contribute - look for the "Edit" buttons! Want to submit a new code implementation? Search for the paper title, and then add the implementation on the paper page https://paperswithcode.com/ you can find a sea of implemented source code with papers. 👉https://t.me/DeepLearning_ai

Artificial Intelligence && Deep Learning
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2021- Courses List of Machine Learning, Deep Learning, and Computer Vision from a top school CS224W: Machine Learning with Graphs - Stanford / Winter 2021 https://www.youtube.com/playlist?list=PLuv1FSpHurUemjLiP4L1x9k6Z9D8rNbYW Full Stack Deep Learning - Spring 2021 - UC Berkeley https://www.youtube.com/playlist?list=PLuv1FSpHurUc2nlabZjCLLe8EQa9fOoa9 Introduction to Deep Learning (I2DL) - Technical University of Munich https://www.youtube.com/playlist?list=PLuv1FSpHurUdmk7v06MDyIx0SDxTrIoqk 3D Computer Vision - National University of Singapore - 2021 https://www.youtube.com/playlist?list=PLuv1FSpHurUflLnJF6hgi0FkeNG1zSFCZ CV3DST - Computer Vision 3: Detection, Segmentation and Tracking https://www.youtube.com/playlist?list=PLuv1FSpHurUd08wNo1FMd3eCUZXm8qexe ADL4CV - Advanced Deep Learning for Computer Vision https://www.youtube.com/playlist?list=PLuv1FSpHurUcQi2CwFIVQelSFCzxphJqz 👉https://t.me/DeepLearning_ai

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Review — PAN: Pyramid Attention Network for Semantic Segmentation (Semantic Segmentation Using FPA & GAU Modules, Outperforms
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Awesome Semantic Segmentation Networks by architecture * U-Net * SegNet * DeepLab * FCN * ENet * LinkNet * DenseNet * Dilated
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ERFNet: Efficient Residual Factorized ConvNet for Real-time Semantic Segmentation [Cited by 452] paper: http://www.robesafe.u
ERFNet: Efficient Residual Factorized ConvNet for Real-time Semantic Segmentation [Cited by 452] paper: http://www.robesafe.uah.es/personal/eduardo.romera/pdfs/Romera17tits.pdf github [PyTorch]: https://github.com/Eromera/erfnet_pytorch

Artificial Intelligence && Deep Learning
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ERFNet: Efficient Residual Factorized ConvNet for Real-time Semantic Segmentation [Cited by 452] paper: http://www.robesafe.uah.es/personal/eduardo.romera/pdfs/Romera17tits.pdf github [PyTorch]: https://github.com/Eromera/erfnet_pytorch

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Review — AdderNet: Do We Really Need Multiplications in Deep Learning? (Image Classification) This is a paper in 2020 CVPR with over 20 citations. ( Sik-Ho Tsang @ Medium) https://sh-tsang.medium.com/review-addernet-do-we-really-need-multiplications-in-deep-learning-image-classification-b72851ddb255 https://t.me/DeepLearning_ai https://t.me/MachineLearning_Programming

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If you want to learn about Data science, machine learning, deep learning, computer vision and Big data. You will get handwritten notes on machine learning, deep learning and artificial intelligence plus you will get access to short notes on each topic in pdf format. You can join here👇👇👇 https://t.me/dataspoof 🔲🔲🔲🔲🔲

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MIT 6.S191 Introduction to Deep Learning 2021 Course Description MIT's introductory course on deep learning methods with appl
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Good day dear subscribers. Today, 25th January, our community are already more than 30K. Within these years we learn or still learning more about specific topics through channel. I try my best to provide, keep going with contemporary knowledge and practice, as well as, keep in touch with things based on #AI, #ML, #DL, #DS, #Python. Thanks for being with us and stay with us and invite your friends (https://t.me/DeepLearning_ai). If you have suggestions to improve the channel's content or related things, please let me know. Thanks @ShohruhRakhmatov