现已上线！2025 年 Telegram 研究 — 年度关键洞察 
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:
显示更多📈 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 58 018 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 58 018 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。
58 018
订阅者
-824 小时
-287 天
-20430 天
帖子存档
Algorithms online Course from PRINCETON UNIVERSITY
About this Course
This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers elementary data structures, sorting, and searching algorithms. Part II focuses on graph- and string-processing algorithms.
All the features of this course are available for free. It does not offer a certificate upon completion
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@DeepLearning_AI
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https://www.coursera.org/learn/algorithms-part1?ranMID=40328&ranEAID=SAyYsTvLiGQ&ranSiteID=SAyYsTvLiGQ-ayH4CcL5jMTprP4tidKo4g&siteID=SAyYsTvLiGQ-ayH4CcL5jMTprP4tidKo4g&utm_content=10&utm_medium=partners&utm_source=linkshare&utm_campaign=SAyYsTvLiGQ
DeepMind & Google Graph Matching Network Outperforms GNN
DeepMind and Google researchers have proposed a powerful new graph matching network (GMN) model for the retrieval and matching of graph structured objects. GMN uses similarity learning for graph structured objects and outperforms graph neural network (GNN) models on graph similarity learning (GSL) tasks.
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@DeepLearning_AI
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https://medium.com/syncedreview/deepmind-google-graph-matching-network-outperforms-gnn-c277d3ca6f75
On Choosing a Deep Reinforcement Learning Library
As Deep Reinforcement Learning is becoming one of the most hyped strategies to achieve AGI (aka Artificial General Intelligence) more and more libraries are developed.
And choosing the best for your needs can be a daunting task…
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@DeepLearning_AI
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https://medium.com/data-from-the-trenches/choosing-a-deep-reinforcement-learning-library-890fb0307092
Fun with Snapchat's Gender Swapping Filter
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@DeepLearning_AI
How to become an expert in NLP in 2019 (1)
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@DeepLearning_AI
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https://medium.com/@kushajreal/how-to-become-an-expert-in-nlp-in-2019-1-945f4e9073c0
My Top 5 Recommended Places to Learn about Deep Learning and Machine Learning
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@DeepLearning_AI
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https://medium.com/datadriveninvestor/my-top-5-recommended-places-to-learn-about-deep-learning-and-machine-learning-f95153a847e
Live Object Detection – Towards Data Science
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@DeepLearning_AI
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https://towardsdatascience.com/live-object-detection-26cd50cceffd
Edge Detection in Opencv 4.0, A 15 Minutes Tutorial
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@DeepLearning_AI
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https://blog.sicara.com/opencv-edge-detection-tutorial-7c3303f10788
A Step-by-Step Introduction to the Basic Object Detection Algorithms
Table of Contents
A Simple Way of Solving an Object Detection Task (using Deep Learning)
Understanding Region-Based Convolutional Neural Networks
1. Intuition of RCNN
2. Problems with RCNN
Understanding Fast RCNN
1. Intuition of Fast RCNN
2. Problems with Fast RCNN
Understanding Faster RCNN
1. Intuition of Faster RCNN
2. Problems with Faster RCNN
Summary of the Algorithms covered
Deep Learning Explained (FREE COURSE)
Welcome to course
1 | Introduction and Overview
2 | Multi-class Classification using Logistic Regression
3 | Multi-Layer Perceptron
4 | Convolution Neural Network
5 | Recurrent Neural Network and Long Short Term Memory
6 | Text Classification with RNN and LSTM
Wrap-up and Post-Course Survey
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@DeepLearning_AI
China is about to overtake America in AI research
China will publish more of the most-cited 50 percent of papers than America for the first time this year
https://www.theverge.com/2019/3/14/18265230/china-is-about-to-overtake-america-in-ai-research
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@DeepLearning_AI
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Machine Learning is Fun!
The world’s easiest introduction to Machine Learning
https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471
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@DeepLearning_AI
TOP 10 FREE DEEP LEARNING MASSIVE OPEN ONLINE COURSES :
1. Deep Learning by Google
2. Neural Networks and Deep Learning
3. Algorithms: Design and Analysis
4. Machine Learning
5. Improving Deep Neural Networks
6. Deep Learning Lecture
7. Neural Networks for Machine Learning
8. Creative Applications of Deep Learning with TensorFlow
9. Introduction to Deep Learning
10. Deep Learning for Self-Driving Cars
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@DeepLearning_AI
https://edgy.app/top-10-free-deep-learning-moocs
Initializing neural networks
Initialization can have a significant impact on convergence in training deep neural networks. Simple initialization schemes can accelerate training, but they require care to avoid common pitfalls. In this post, we’ll explain how to initialize neural network parameters effectively.
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@DeepLearning_AI
https://www.deeplearning.ai/ai-notes/initialization/
