AI and Machine Learning

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Learn Data Science, Data Analysis, Machine Learning, Artificial Intelligence, and Python with Tensorflow, Pandas & more! Buy ads: https://telega.io/c/machine_learning_courses

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印度2 913 教育1 493...

📈 Telegram 频道 AI and Machine Learning 的分析概览

频道 AI and Machine Learning (@machine_learning_courses) 英语语言赛道中的是活跃参与者。目前社区聚集了 95 477 名订阅者，在教育类别中位列第 1 493，并在印度地区排名第 2 913 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 95 477 名订阅者。

根据 15 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 356，过去 24 小时变化为 24，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 9.36%。内容发布后 24 小时内通常能获得 2.40% 的反应，占订阅者总量。
帖子覆盖： 每篇帖子平均可获得 8 940 次浏览，首日通常累积 2 287 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 learning, llm, linkedin, linux, udemy 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：
“Learn Data Science, Data Analysis, Machine Learning, Artificial Intelligence, and Python with Tensorflow, Pandas & more! Buy ads: https://telega.io/c/machine_learning_courses”

凭借高频更新（最新数据采集于 16 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，使其成为教育类别中的关键影响点。

95 477

订阅者

+2424 小时

+867 天

+35630 天

8 940

帖子浏览量

~ 2 28724 小时

~ 3 00648 小时

9.36%

参与率

~ 1

每日帖子数

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日期	订阅者增长	提及	频道
16 九月	+5
15 九月	+28
14 九月	+11
13 九月	+8
12 九月	+10
11 九月	+13
10 九月	+20
09 九月	+17
08 九月	+17
07 九月	+26
06 九月	+22
05 九月	+17
04 九月	+45
03 九月	+33
02 九月	+24
01 九月	+6

频道帖子

Colorizing old black-and-white videos and "bringing faces to life" for FREE SVFR — a full-fledged framework for restoring faces in videos. It can: 💬 BFR — improve blurry faces. 💬 Colorization — colorize black-and-white videos. 💬 Inpainting — redraw damaged areas. 💬 and combine all of this in one pass. Essentially, the model takes old or damaged videos and makes them "as if they were shot yesterday". And it's free and open-source. ⚙️ Installation locally: 1. Create an environment

conda create -n svfr python=3.9 -y
conda activate svfr

2. Install PyTorch (for your CUDA)

pip install torch==2.2.2 torchvision==0.17.2 torchaudio==2.2.2

3. Install dependencies

pip install -r requirements.txt

4. Download models

conda install git-lfs
git lfs install
git clone https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt models/stable-video-diffusion-img2vid-xt

5. Start processing videos

python infer.py \
--config config/infer.yaml \
--task_ids 0 \
--input_path input.mp4 \
--output_dir results/ \
--crop_face_region

Where task_ids: * 0 — face enhancement * 1 — colorization * 2 — redrawing damage An ideal tool if: 🟢you're restoring archival videos; 🟢you're creating historical content; 🟢you're working with neural networks and video effects; 🟢you want a wow result without paid services. ▶️ Demo on Hugging Face 🌐 GitHub/Instructions

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6	📌 A comprehensive masterclass on Claude Code is available via this repository: https://github.com/luongnv89/claude-howto. This resource provides a detailed visual and practical guide for one of the most powerful tools for developers. The repository includes: • Step-by-step learning paths covering basic commands (/init, /plan) to advanced features such as MCP, hooks, and agents, achievable in approximately 11–13 hours. 📚 • An extensive library of custom commands designed for real-world tasks. • Ready-made memory templates for both individual and team workflows. • Instructions and scripts for: - Automated code review. - Style and standards compliance checks. - API documentation generation. • Automation cycles enabling autonomous operation of Claude without direct user intervention. ⚙️ • Integration with external tools, including GitHub and various APIs, presented with step-by-step guidance. • Diagrams and charts to facilitate understanding, suitable for beginners. 📊 • Examples for configuring highly specialized sub-agents. • Dedicated learning scripts, such as tools for generating educational books and materials to master specific topics efficiently. Access the full guide here: https://github.com/luongnv89/claude-howto	8 235
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13	🔅 Deep Learning with TensorFlow: Insights and Innovations 📝 Explore the evolving world of deep learning with TensorFlow, including the basics of generative AI, with practical, hands-on examples. 🌐 Author: Isil Berkun 🔰 Level: Intermediate ⏰ Duration: 3h 6m 📋 Topics: TensorFlow, Deep Learning, Artificial Intelligence 🔗 Join Artificial intelligence for more courses	8 367
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20	+1 AI Agents vs Agentic AI... what’s the actual difference? There are 3 types of AI workflows worth knowing and each performs a different task. If you don’t understand these you’re probably falling behind. Non-Agentic AI: Basic prompt-response AI with no memory/reasoning. They’re fast, cheap, and universally accessible, requires no technical build or integration and great for clear, one-off tasks. Agentic AI: Self-managing AI system that can plan and execute. Great for handling complex, changing projects. They can integrate with tools and databases and produce more reliable outcomes. AI Agent: A single-task AI worker designed to automate one task. Automates repetitive, time-consuming tasks, quick setup and cost-efficient and easy to test and refine within roles In short: AI Agents = Single-task automation Agentic AI = Multi-step problem solving	8 899

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