متاح الآن! بحث تيليغرام 2025 — أهم رؤى العام 
Artificial Intelligence
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🔒 Welcome Artificial Intelligence Channel Buy ads: https://telega.io/c/Artificial_Intelligence_COM
إظهار المزيد📈 نظرة تحليلية على قناة تيليجرام Artificial Intelligence
تُعد قناة Artificial Intelligence (@artificial_intelligence_com) في القطاع اللغوي الإنكليزية لاعباً نشطاً. يضم المجتمع حالياً 70 419 مشتركاً، محتلاً المرتبة 1 849 في فئة التكنولوجيات والتطبيقات والمرتبة 4 785 في منطقة الهند.
📊 مؤشرات الجمهور والحراك
منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 70 419 مشتركاً.
بحسب آخر البيانات بتاريخ 13 يونيو, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار 1 217، وفي آخر 24 ساعة بمقدار 69، مع بقاء الوصول العام مرتفعاً.
- حالة التحقق: غير موثّقة
- معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 7.35%. وخلال أول 24 ساعة من النشر يحصد المحتوى عادةً 2.09% من ردود الفعل نسبةً إلى إجمالي المشتركين.
- وصول المنشورات: يحصل كل منشور على متوسط 5 179 مشاهدة. وخلال اليوم الأول يجمع عادةً 1 474 مشاهدة.
- التفاعلات والاستجابة: يتفاعل الجمهور بانتظام؛ متوسط التفاعلات لكل منشور يبلغ 10.
- الاهتمامات الموضوعية: يركز المحتوى على مواضيع رئيسية مثل learning, linkedin, linux, udemy, 040k|.
📝 الوصف وسياسة المحتوى
يصف المؤلف القناة بأنها مساحة للتعبير عن الآراء الذاتية:
“🔒 Welcome Artificial Intelligence Channel
Buy ads: https://telega.io/c/Artificial_Intelligence_COM”
بفضل وتيرة التحديث المرتفعة (أحدث البيانات بتاريخ 14 يونيو, 2026) تحافظ القناة على حداثتها ومستوى وصول مرتفع. وتُظهر التحليلات تفاعلاً نشطاً من الجمهور، ما يجعلها نقطة تأثير مهمة ضمن فئة التكنولوجيات والتطبيقات.
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🔗 30 Useful AI Apps That Can Help You in 2025
AI apps are taking over the world. There’s an AI app for every conceivable use case. Here are some AI apps for different categories:1 - General Purpose: Perplexity, Anthropic Claude, Grok, ChatGPT, and Gemini 2 - Writing Code: Cursor, Replit, Windsurf AI, Github Copilot, and Tabnine 3 - Productivity: Adobe (PDF Chat), Gemini for Gmail, Gamma (AI slide deck), WisprFlow (AI voice dictation), and Granola (AI notetaker) 4 - Audience Building: Delphi (AI text, voice), HeyGen (video translation), Persona (AI agent builder), Captions (AI video editing), and OpusClips (Video repurposing) 5 - Creativity: ElevenLabs (realistic AI voices), Midjourney, Suno AI (music generation), Krea (enhance images), and Photoroom (AI image editing) 6 - Learning and Growth: Particle News App, Rosebud (AI journal app), NotebookLM, GoodInside (parenting co-pilot), and Ash (AI counselor).
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📱Artificial Intelligence and Machine Learning
📱Artificial Intelligence Foundations: Machine Learning
70 430
📂 Full description
Machine learning is the most exciting branch of artificial intelligence. It allows systems to learn from data by identifying patterns and making decisions with little to no human intervention. In this course, you'll navigate the machine learning lifecycle by getting hands-on practice training your first machine learning model. Join instructor Kesha Williams as she explores widely adopted machine learning methods: supervised, unsupervised, and reinforcement. There's a focus on sourcing and preparing data and selecting the best learning algorithm for your project. After training a model, learn to evaluate model performance using standard metrics. Finally, Kesha shows you how to streamline the process by building a machine learning pipeline. If youre looking to understand the machine learning lifecycle and the steps required to build systems, check out this course.
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🔅 Artificial Intelligence Foundations: Machine Learning
🌐 Author: Kesha Williams
🔰 Level: Beginner
⏰ Duration: 1h 50m
🌀 Learn about the machine learning lifecycle and the steps required to build systems in this hands-on course.📗 Topics: Machine Learning, Artificial Intelligence 📤 Join Artificial Intelligence and Machine Learning for more courses
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8. Set up the user interface and trigger the main function.
• Provides an input field for the user's question
• Triggers the main function when the user clicks "Get Answer"
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7. Define the main function to run all LLMs and aggregate results.
• Runs all reference models asynchronously
• Displays individual responses in expandable sections
• Aggregates responses using the aggregator model
• Streams the aggregated response.
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6. Implement the LLM call function.
• Asynchronously calls the LLM with the user's prompt
• Returns the model name and its response
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5. Define the models and aggregator system prompt.
• Specifies the LLMs to be used for generating responses
• Defines the aggregator model and its system prompt
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4. Initialize Together AI clients.
• Sets up Together API key as an environment variable
• Initializes both synchronous and asynchronous Together clients
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3. Set up the Streamlit app and API key input.
• Creates a title for the app
• Adds a secure input field for the Together API key
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2. Import necessary libraries
• Streamlit for the web interface
• asyncio for asynchronous operations
• Together AI for LLM interactions
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1. Install the necessary Python Libraries
Run the following commands from your terminal to install the required libraries:
