Machine Learning

前往频道在 Telegram

Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho

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网络:Machine Learning with Python 叙利亚231 技术与应用3 384...

📈 Telegram 频道 Machine Learning 的分析概览

频道 Machine Learning (@machinelearning9) 英语语言赛道中的是活跃参与者。目前社区聚集了 40 106 名订阅者，在 技术与应用 类别中位列第 3 384，并在 叙利亚 地区排名第 231 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 40 106 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 401，过去 24 小时变化为 38，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 1.96%。内容发布后 24 小时内通常能获得 1.16% 的反应，占订阅者总量。
帖子覆盖： 每篇帖子平均可获得 788 次浏览，首日通常累积 465 次浏览。
互动与反馈： 受众积极参与，单帖平均反应数为 2。
主题关注点： 内容集中在 distance, insidead, gpu, learning, degree 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：
“Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho”

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。

40 106

订阅者

+3824 小时

+637 天

+40130 天

788

帖子浏览量

~ 46524 小时

~ 62048 小时

1.96%

参与率

~ 3

每日帖子数

Ads index

beta

帖子存档

40 106

📌 Why Healthcare Leads in Knowledge Graphs 🗂 Category: DATA SCIENCE 🕒 Date: 2026-01-18 | ⏱️ Read time: 9 min read How science, regulation, collaboration, and public funding shaped the world’s most mature semantic infrastructure #DataScience #AI #Python

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Adakah anda merasakan analisis anda sentiasa kekurangan rangka kerja？Kami telah menubuhkan forum perbincangan mendalam yang memberi t Adakah anda merasakan analisis anda sentiasa kekurangan rangka kerja？Kami telah menubuhkan forum perbincangan mendalam yang memberi t Sponsored By WaybienAds

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📌 The Hidden Opportunity in AI Workflow Automation with n8n for Low-Tech Companies 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-01-18 | ⏱️ Read time: 14 min read How to use n8n with multimodal AI and optimisation tools to help companies with low… #DataScience #AI #Python

40 106

Repost from Machine Learning with Python

Best GitHub repositories to learn AI from scratch in 2026:

1. Andrej Karpathy https://github.com/karpathy/nn-zero-to-hero 2. Hugging Face Transformers https://github.com/huggingface/transformers 3. FastAI/fastbook https://github.com/fastai/fastbook 4. Made-With-ML https://github.com/GokuMohandas/Made-With-ML 5. ML System Design https://github.com/chiphuyen/machine-learning-systems-design 6. Awesome Generative AI guide https://github.com/aishwaryanr/awesome-generative-ai-guide 7. Dive into Deep Learning https://github.com/d2l-ai/d2l-en 🪞 @codeprogrammer Like & Share

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📌 A Geometric Method to Spot Hallucinations Without an LLM Judge 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-01-17 | ⏱️ Read time: 7 min read Imagine a flock of birds in flight. There’s no leader. No central command. Each bird… #DataScience #AI #Python

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📌 Data Poisoning in Machine Learning: Why and How People Manipulate Training Data 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-01-17 | ⏱️ Read time: 14 min read Do you know where your data has been? #DataScience #AI #Python

40 106

Repost from Machine Learning with Python

🤖 Machine Learning Tutorials Repository 1. Python 2. Computer Vision: Techniques, algorithms 3. NLP 4. Matplotlib 5. NumPy 6. Pandas 7. MLOps 8. LLMs 9. PyTorch/TensorFlow git clone https://github.com/patchy631/machine-learning 🔗 GitHub: https://github.com/patchy631/machine-learning/tree/main ⭐️ https://t.me/DataScienceT

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📌 The Great Data Closure: Why Databricks and Snowflake Are Hitting Their Ceiling 🗂 Category: DATA SCIENCE 🕒 Date: 2026-01-16 | ⏱️ Read time: 13 min read Acquisitions, venture, and an increasingly competitive landscape all point to a market ceiling #DataScience #AI #Python

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📌 From RGB to Lab: Addressing Color Artifacts in AI Image Compositing 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-01-16 | ⏱️ Read time: 13 min read A multi-tier approach to segmentation, color correction, and domain-specific enhancement #DataScience #AI #Python

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📌 Cutting LLM Memory by 84%: A Deep Dive into Fused Kernels 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-01-16 | ⏱️ Read time: 18 min read Why your final LLM layer is OOMing and how to fix it with a custom… #DataScience #AI #Python

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👍 Top Channels on Telegram 🌟

40 106

Repost from Machine Learning with Python

YOLO Training Template Manual data labeling has become significantly more convenient. Now the process looks like in the usual labeling systems - you just outline the object with a frame and a bounding box is immediately created. The platform allows: • to upload your own dataset • to label manually or auto-label via DINOv3 • to enrich the data if desired • to train a #YOLO model on your own data • to run inference immediately • to export to ONNX or NCNN, which ensures compatibility with edge hardware and smartphones All of this is available for free and can already be tested on #GitHub. Repo: https://github.com/computer-vision-with-marco/yolo-training-template https://t.me/CodeProgrammer

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📌 Maximum-Effiency Coding Setup 🗂 Category: PROGRAMMING 🕒 Date: 2026-01-16 | ⏱️ Read time: 9 min read Learn how to be a more efficient programmer #DataScience #AI #Python

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📌 Do You Smell That? Hidden Technical Debt in AI Development 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-01-15 | ⏱️ Read time: 14 min read Why speed without standards creates fragile AI products #DataScience #AI #Python

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📌 The 2026 Goal Tracker: How I Built a Data-Driven Vision Board Using Python, Streamlit, and Neon 🗂 Category: PRODUCTIVITY 🕒 Date: 2026-01-15 | ⏱️ Read time: 8 min read Designing a centralized system to track daily habits and long-term goals #DataScience #AI #Python

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📌 How to Run Coding Agents in Parallel 🗂 Category: AGENTIC AI 🕒 Date: 2026-01-15 | ⏱️ Read time: 8 min read Get the most out of Claude Code #DataScience #AI #Python

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📌 When Shapley Values Break: A Guide to Robust Model Explainability 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-01-15 | ⏱️ Read time: 9 min read Shapley Values are one of the most common methods for explainability, yet they can be… #DataScience #AI #Python