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 58 029 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 58 029 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。

58 029

订阅者

+1724 小时

-237 天

-19330 天

5 467

帖子浏览量

无数据24 小时

无数据48 小时

9.42%

参与率

无数据

每日帖子数

Ads index

beta

帖子存档

58 019

Must-read papers on GNN Join👇👇👇 @DeepLearning_AI .https://github.com/thunlp/GNNPapers

58 019

Review: FSRCNN (Super Resolution) What Are Covered 1. Brief Review of SRCNNFSRCNN Network 2. ArchitectureExplanation of 1×1 Convolution Used in 3. Shrinking and Expanding 4. Explanation of Multiple 3×3 Convolutions in Non-Linear Mapping 5. Ablation Study 6. Results Join👇👇👇 @DeepLearning_AI .https://towardsdatascience.com/review-fsrcnn-super-resolution-80ca2ee14da4

58 019

Review: G-RMI — Winner in 2016 COCO Detection (Object Detection) Join👇👇👇 @DeepLearning_AI .https://towardsdatascience.com/review-g-rmi-winner-in-2016-coco-detection-object-detection-af3f2eaf87e4

58 019

The project is about predicting coronary heart disease by using three different ML algorithms Join👇👇👇 https://blog.goodaudience.com/heart-disease-prediction-aa656f2db585

58 019

The 5 Feature Selection Algorithms every Data Scientist should know Join👇👇👇 @DeepLearning_AI .https://towardsdatascience.com/the-5-feature-selection-algorithms-every-data-scientist-need-to-know-3a6b566efd2

58 019

Deepfakes, FaceGANS, and Synthetic Data: Welcome to the Reality Illusion of 2020 Join👇👇👇 @DeepLearning_AI .https://medium.com/swlh/deepfakes-facegans-and-the-rise-of-synthetic-data-welcome-to-2020-a54b88eecdf9

58 019

2D or 3D? A Simple Comparison of Convolutional Neural Networks for Automatic Segmentation of Cardiac Imaging 👇👇👇 @DeepLearning_AI .https://towardsdatascience.com/2d-or-3d-a-simple-comparison-of-convolutional-neural-networks-for-automatic-segmentation-of-625308f52aa7

58 019

A 2019 Guide to Object Detection 👇👇👇 @DeepLearning_AI .https://heartbeat.fritz.ai/a-2019-guide-to-object-detection-9509987954c3

58 019

Everything You Need to Know About Autoencoders in TensorFlow Join👇👇👇 @DeepLearning_AI .https://towardsdatascience.com/everything-you-need-to-know-about-autoencoders-in-tensorflow-b6a63e8255f0

58 019

The Best Machine Learning Research of 2019 So Far - ODSC - Open Data Science - Medium Join👇👇👇 @DeepLearning_AI .https://medium.com/@ODSC/the-best-machine-learning-research-of-2019-so-far-954120947794

58 019

Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library 1st Edition join👇👇👇 @DeepLearning_AI Who This Book Is For This book contains descriptions, working code examples, and explanations of the C++ computer vision tools contained in the OpenCV 3.x library. Thus, it should be helpful to many different kinds of users: Professionals and entrepreneurs For practicing professionals who need to rapidly prototype or professionally implement computer vision systems, the sample code provides a quick frame‐ work with which to start. Our descriptions of the algorithms can quickly teach or remind the reader how they work. OpenCV 3.x sits on top of a hardware acceler‐ ation layer (HAL) so that implemented algorithms can run efficiently, seamlessly taking advantage of a variety of hardware platforms. Students.... Teachers.... Hobbyist....

58 019

Video classification with Keras and Deep Learning join👇👇👇 @DeepLearning_AI .https://www.pyimagesearch.com/2019/07/15/video-classification-with-keras-and-deep-learning/

58 019

Advancing Semi-supervised Learning with Unsupervised Data Augmentation join👇👇👇 @DeepLearning_AI .https://ai.googleblog.com/2019/07/advancing-semi-supervised-learning-with.html

58 019

FREE COURSE Intro to TensorFlow for Deep Learning This course is a practical approach to deep learning for software developers join👇👇👇 @DeepLearning_AI .https://www.udacity.com/course/intro-to-tensorflow-for-deep-learning--ud187

58 019

Your State of AI Report 2019 join👇👇👇 @DeepLearning_AI .https://medium.com/@NathanBenaich/state-of-ai-2019-733c81aeb04b

58 019

Landmark Assisted CycleGAN for Cartoon Face Generation join👇👇👇 @DeepLearning_AI .https://deepai.org/publication/landmark-assisted-cyclegan-for-cartoon-face-generation

58 019

The Secret to Mastering ML join👇👇👇 @DeepLearning_AI .https://medium.com/technomancy/the-secret-to-mastering-ml-9e328d5f06a

58 019

500+ awesome bookmarks for Data Science & Machine Learning join👇👇👇 @DeepLearning_AI .https://towardsdatascience.com/500-free-high-quality-online-resources-for-data-science-machine-learning-7eda5bf33872

58 019

Computer Vision: A Study On Different CNN Architectures and their Applications join👇👇👇 @DeepLearning_AI .https://medium.com/alumnaiacademy/introduction-to-computer-vision-4fc2a2ba9dc

58 019

AI Scholar: Human vs Machine Attention in Neural Networks join👇👇👇 @DeepLearning_AI .https://medium.com/ai%C2%B3-theory-practice-business/ai-scholar-human-vs-machine-attention-in-neural-networks-69a8b59e577f