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5 977 技术与应用2 290...

📈 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 58 019 名订阅者，在 技术与应用 类别中位列第 2 290，并在印度地区排名第 5 977 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 58 019 名订阅者。

根据 25 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 -204，过去 24 小时变化为 -8，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 9.58%。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 5 556 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 26 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。

58 019

订阅者

-824 小时

-287 天

-20430 天

5 556

帖子浏览量

无数据24 小时

无数据48 小时

9.58%

参与率

无数据

每日帖子数

Ads index

beta

帖子存档

58 019

650 Free Online Programming & Computer Science Courses You Can Start This July join👇👇👇 @DeepLearning_AI . https://www.freecodecamp.org/news/650-free-online-programming-computer-science-courses-you-can-start-this-summer/

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Review: FCN — Fully Convolutional Network (Semantic Segmentation) Covered: * From Image Classification to Semantic Segmentation * Upsampling Via Deconvolution * Fusing the Output * Results join👇👇👇 @DeepLearning_AI . https://towardsdatascience.com/review-fcn-semantic-segmentation-eb8c9b50d2d1

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Implement Back Propagation in Neural Networks join👇👇👇 @DeepLearning_AI . https://medium.com/coinmonks/implement-back-propagation-in-neural-networks-ed09897593e7

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1. 10 New Things I Learnt from fast.ai v3 2. 2019 deep learning course Practical Deep Learning for Coders, v3. 10 learning points as such: 1. The Universal Approximation Theorem 2. Neural Networks: Design & Architecture 3. Understanding the Loss Landscape 4. Gradient Descent Optimisers 5. Loss Functions 6. Training 7. Regularisation 8. Tasks 9. Model Interpretability 10. Appendix: Jeremy Howard on Model Complexity & Regularisation join👇👇👇 @DeepLearning_AI https://towardsdatascience.com/10-new-things-i-learnt-from-fast-ai-v3-4d79c1f07e33

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Few-Shot Adversarial Learning of Realistic Neural Talking Head Models paper — arxiv👇👇👇 https://arxiv.org/pdf/1905.08233.pdf video — youtube👇👇👇 https://www.youtube.com/watch?v=p1b5aiTrGzY join channel 👇👇👇 @DeepLearning_AI .

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Few-Shot Adversarial Learning of Realistic Neural Talking Head Models 👇👇👇👇👇 @DeepLearning_AI

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Few-Shot Adversarial Learning of Realistic Neural Talking Head Models 👇👇👇👇 @DeepLearning_AI

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Stanford Machine Learning Content 01 and 02: Introduction, Regression Analysis and Gradient Descent 03: Linear Algebra - review 04: Linear Regression with Multiple Variables 05: Octave[incomplete] 06: Logistic Regression 07: Regularization 08: Neural Networks - Representation 09: Neural Networks - Learning 10: Advice for applying machine learning techniques 11: Machine Learning System Design 12: Support Vector Machines 13: Clustering 14: Dimensionality Reduction 15: Anomaly Detection 16: Recommender Systems 17: Large Scale Machine Learning 18: Application Example - Photo OCR 19: Course Summary http://www.holehouse.org/mlclass/ 👇👇👇👇👇 @DeepLearning_AI

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Deep Learning lecture The full deck of (600+) slides, by Gilles Louppe: 👇👇👇👇👇 @DeepLearning_AI . https://glouppe.github.io/info8010-deep-learning/pdf/lec-all.pdf

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Deep learning lecture

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Diving into Deep Convolutional Semantic Segmentation Networks and Deeplab_V3 👇👇👇👇👇 @DeepLearning_AI . https://sthalles.github.io/deep_segmentation_network/