متاح الآن! بحث تيليغرام 2025 — أهم رؤى العام 
Github Top Repositories
الذهاب إلى القناة على Telegram
Top GitHub repositories in one place 🚀 Explore the best projects in programming, AI, data science, and more.
إظهار المزيد📈 نظرة تحليلية على قناة تيليجرام Github Top Repositories
تُعد قناة Github Top Repositories (@githubre) في القطاع اللغوي الإنكليزية لاعباً نشطاً. يضم المجتمع حالياً 13 330 مشتركاً، محتلاً المرتبة 15 272 في فئة التعليم والمرتبة 32 126 في منطقة الهند.
📊 مؤشرات الجمهور والحراك
منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 13 330 مشتركاً.
بحسب آخر البيانات بتاريخ 15 يونيو, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار 413، وفي آخر 24 ساعة بمقدار 8، مع بقاء الوصول العام مرتفعاً.
- حالة التحقق: غير موثّقة
- معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 1.07%. وخلال أول 24 ساعة من النشر يحصد المحتوى عادةً 0.79% من ردود الفعل نسبةً إلى إجمالي المشتركين.
- وصول المنشورات: يحصل كل منشور على متوسط 143 مشاهدة. وخلال اليوم الأول يجمع عادةً 105 مشاهدة.
- التفاعلات والاستجابة: يتفاعل الجمهور بانتظام؛ متوسط التفاعلات لكل منشور يبلغ 1.
- الاهتمامات الموضوعية: يركز المحتوى على مواضيع رئيسية مثل repository, fork, programming, statistic, description.
📝 الوصف وسياسة المحتوى
يصف المؤلف القناة بأنها مساحة للتعبير عن الآراء الذاتية:
“Top GitHub repositories in one place 🚀
Explore the best projects in programming, AI, data science, and more.”
بفضل وتيرة التحديث المرتفعة (أحدث البيانات بتاريخ 16 يونيو, 2026) تحافظ القناة على حداثتها ومستوى وصول مرتفع. وتُظهر التحليلات تفاعلاً نشطاً من الجمهور، ما يجعلها نقطة تأثير مهمة ضمن فئة التعليم.
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المشتركون
+824 ساعات
+927 أيام
+41330 أيام
أرشيف المشاركات
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We offer you daily Udemy courses for free and without any fees.
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13 330
We offer you daily Udemy courses for free and without any fees.
https://t.me/DataScienceC
13 330
We offer you daily Udemy courses for free and without any fees.
https://t.me/DataScienceC
13 330
We offer you daily Udemy courses for free and without any fees.
https://t.me/DataScienceC
13 330
Repost from Machine Learning with Python
We offer you daily Udemy courses for free and without any fees.
https://t.me/DataScienceC
13 330
python-docx: Create and Modify Word Documents #python
python-docx is a Python library for reading, creating, and updating Microsoft Word 2007+ (.docx) files.
Installation
pip install python-docxfrom docx import Document
document = Document()
document.add_paragraph("It was a dark and stormy night.")
<docx.text.paragraph.Paragraph object at 0x10f19e760>
document.save("dark-and-stormy.docx")
document = Document("dark-and-stormy.docx")
document.paragraphs[0].text
'It was a dark and stormy night.'13 330
🧱 AI now generates worlds in the style of Minecraft — presenting the GameFactory model
Researchers trained the model on 70 hours of Minecraft gameplay and achieved impressive results:
GameFactory can create procedural game worlds — from volcanoes to cherry blossom forests, just like in the iconic simulator.
🔥 Want your own endless world? Just set the parameters.
🟠 Examples and code — at the link: https://yujiwen.github.io/gamefactory/
🟠Github: https://github.com/KwaiVGI/GameFactory
https://t.me/DataScienceN 🌟
13 330
Repost from Machine Learning with Python
This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages
✅ https://t.me/addlist/8_rRW2scgfRhOTc0
✅ https://t.me/Codeprogrammer
13 330
LangExtract
A Python library for extracting structured information from unstructured text using LLMs with precise source grounding and interactive visualization.
GitHub: https://github.com/google/langextract
https://t.me/DataScience4 🖕
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html-to-markdown
A modern, fully typed Python library for converting HTML to Markdown. This library is a completely rewritten fork of markdownify with a modernized codebase, strict type safety and support for Python 3.9+.
Features:
⭐️ Full HTML5 Support: Comprehensive support for all modern HTML5 elements including semantic, form, table, ruby, interactive, structural, SVG, and math elements
⭐️ Enhanced Table Support: Advanced handling of merged cells with rowspan/colspan support for better table representation
⭐️ Type Safety: Strict MyPy adherence with comprehensive type hints
Metadata Extraction: Automatic extraction of document metadata (title, meta tags) as comment headers
⭐️ Streaming Support: Memory-efficient processing for large documents with progress callbacks
⭐️ Highlight Support: Multiple styles for highlighted text (<mark> elements)
⭐️ Task List Support: Converts HTML checkboxes to GitHub-compatible task list syntax
nstallation
pip install html-to-markdownpip install html-to-markdown[lxml]from html_to_markdown import convert_to_markdown
html = """
<!DOCTYPE html>
<html>
<head>
<title>Sample Document</title>
<meta name="description" content="A sample HTML document">
</head>
<body>
<article>
<h1>Welcome</h1>
<p>This is a <strong>sample</strong> with a <a href="https://example.com">link</a>.</p>
<p>Here's some <mark>highlighted text</mark> and a task list:</p>
<ul>
<li><input type="checkbox" checked> Completed task</li>
<li><input type="checkbox"> Pending task</li>
</ul>
</article>
</body>
</html>
"""
markdown = convert_to_markdown(html)
print(markdown)from bs4 import BeautifulSoup
from html_to_markdown import convert_to_markdown
# Configure BeautifulSoup with your preferred parser
soup = BeautifulSoup(html, "lxml") # Note: lxml requires additional installation
markdown = convert_to_markdown(soup)13 330
🎁⏳These 6 steps make every future post on LLMs instantly clear and meaningful.
Learn exactly where Web Scraping, Tokenization, RLHF, Transformer Architectures, ONNX Optimization, Causal Language Modeling, Gradient Clipping, Adaptive Learning, Supervised Fine-Tuning, RLAIF, TensorRT Inference, and more fit into the LLM pipeline.
﹌﹌﹌﹌﹌﹌﹌﹌﹌
》 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗟𝗟𝗠𝘀: 𝗧𝗵𝗲 𝟲 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗦𝘁𝗲𝗽𝘀
✸ 1️⃣ Data Collection (Web Scraping & Curation)
☆ Web Scraping: Gather data from books, research papers, Wikipedia, GitHub, Reddit, and more using Scrapy, BeautifulSoup, Selenium, and APIs.
☆ Filtering & Cleaning: Remove duplicates, spam, broken HTML, and filter biased, copyrighted, or inappropriate content.
☆ Dataset Structuring: Tokenize text using BPE, SentencePiece, or Unigram; add metadata like source, timestamp, and quality rating.
✸ 2️⃣ Preprocessing & Tokenization
☆ Tokenization: Convert text into numerical tokens using SentencePiece or GPT’s BPE tokenizer.
☆ Data Formatting: Structure datasets into JSON, TFRecord, or Hugging Face formats; use Sharding for parallel processing.
✸ 3️⃣ Model Architecture & Pretraining
☆ Architecture Selection: Choose a Transformer-based model (GPT, T5, LLaMA, Falcon) and define parameter size (7B–175B).
☆ Compute & Infrastructure: Train on GPUs/TPUs (A100, H100, TPU v4/v5) with PyTorch, JAX, DeepSpeed, and Megatron-LM.
☆ Pretraining: Use Causal Language Modeling (CLM) with Cross-Entropy Loss, Gradient Checkpointing, and Parallelization (FSDP, ZeRO).
☆ Optimizations: Apply Mixed Precision (FP16/BF16), Gradient Clipping, and Adaptive Learning Rate Schedulers for efficiency.
✸ 4️⃣ Model Alignment (Fine-Tuning & RLHF)
☆ Supervised Fine-Tuning (SFT): Train on high-quality human-annotated datasets (InstructGPT, Alpaca, Dolly).
☆ Reinforcement Learning from Human Feedback (RLHF): Generate responses, rank outputs, train a Reward Model (PPO), and refine using Proximal Policy Optimization (PPO).
☆ Safety & Constitutional AI: Apply RLAIF, adversarial training, and bias filtering.
✸ 5️⃣ Deployment & Optimization
☆ Compression & Quantization: Reduce model size with GPTQ, AWQ, LLM.int8(), and Knowledge Distillation.
☆ API Serving & Scaling: Deploy with vLLM, Triton Inference Server, TensorRT, ONNX, and Ray Serve for efficient inference.
☆ Monitoring & Continuous Learning: Track performance, latency, and hallucinations;
✸ 6️⃣Evaluation & Benchmarking
☆ Performance Testing: Validate using HumanEval, HELM, OpenAI Eval, MMLU, ARC, and MT-Bench.
≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣
https://t.me/DataScienceM ⭐️
13 330
Want to learn Python quickly and from scratch? Then here’s what you need — CodeEasy: Python Essentials
🔹Explains complex things in simple words
🔹Based on a real story with tasks throughout the plot
🔹Free start
Ready to begin? Click https://codeeasy.io/course/python-essentials 🌟
👉 @DataScience4
13 330
Repost from Machine Learning with Python
🥇 This repo is like gold for every data scientist!
✅ Just open your browser; a ton of interactive exercises and real experiences await you. Any question about statistics, probability, Python, or machine learning, you'll get the answer right there! With code, charts, even animations. This way, you don't waste time, and what you learn really sticks in your mind!
⬅️ Data science statistics and probability topics
⬅️ Clustering
⬅️ Principal Component Analysis (PCA)
⬅️ Bagging and Boosting techniques
⬅️ Linear regression
⬅️ Neural networks and more...
┌ 📂 Int Data Science Python Dash
└ 🐱 GitHub-Repos
👉 @codeprogrammer
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Repost from Machine Learning with Python
This repository contains a collection of everything needed to work with libraries related to AI and LLM.
More than 120 libraries, sorted by stages of LLM development:
→ Training, fine-tuning, and evaluation of LLM models
→ Integration and deployment of applications with LLM and RAG
→ Fast and scalable model launching
→ Working with data: extraction, structuring, and synthetic generation
→ Creating autonomous agents based on LLM
→ Prompt optimization and ensuring safe use in production
🌟 link: https://github.com/Shubhamsaboo/awesome-llm-apps
👉 @codeprogrammer
13 330
Repost from Machine Learning with Python
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Repost from Machine Learning with Python
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A useful find on GitHub CheatSheets-for-Developers
LINK: https://github.com/crescentpartha/CheatSheets-for-Developers
This is a huge collection of cheat sheets for a wide variety of technologies:
JavaScript, Python, Git, Docker, SQL, Linux, Regex, and many others.Conveniently structured — you can quickly find the topic you need. Save it and use it 🔥 👉 @DATASCIENCEN
