Endi mavjud! Telegram Tadqiqoti 2025 — yilning asosiy insaytlari 
AI and Machine Learning
Kanalga Telegram’da o‘tish
Learn Data Science, Data Analysis, Machine Learning, Artificial Intelligence, and Python with Tensorflow, Pandas & more! Buy ads: https://telega.io/c/machine_learning_courses
Ko'proq ko'rsatish📈 Telegram kanali AI and Machine Learning analitikasi
AI and Machine Learning (@machine_learning_courses) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 94 021 obunachidan iborat bo'lib, Taʼlim toifasida 1 561-o'rinni va Hindiston mintaqasida 3 020-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 94 021 obunachiga ega bo‘ldi.
24 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 986 ga, so‘nggi 24 soatda esa 67 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 6.50% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.56% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 6 109 marta ko‘riladi; birinchi sutkada odatda 1 470 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 8 ta reaksiya keladi.
- Tematik yo‘nalishlar: Kontent learning, llm, linkedin, linux, udemy kabi asosiy mavzularga jamlangan.
📝 Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“Learn Data Science, Data Analysis, Machine Learning, Artificial Intelligence, and Python with Tensorflow, Pandas & more!
Buy ads: https://telega.io/c/machine_learning_courses”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 25 Iyun, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Taʼlim toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.
94 021
Obunachilar
+6724 soatlar
+1517 kunlar
+98630 kunlar
Postlar arxiv
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🔅 Complete Guide to Data Lakes and Lakehouses
📝 Build the foundational knowledge and practical skills essential for data engineers, data scientists, and related professionals to effectively design and utilize data lakes.
🌐 Author: Thalia Barrera
🔰 Level: Advanced
⏰ Duration: 5h 39m
📋 Topics: Data Lakes, Data Engineering, Artificial Intelligence
🔗 Join Artificial intelligence for more courses
94 035
Repost from How AI Helps
Local AI Stack in 2026: what you can actually run on a laptop for text, video, RAG and notebooks
Main point: local AI is no longer a weekend toy. The useful setup is not the biggest model, but the right model for the job and hardware.
🧩 Text: start with
Qwen3-4B/8B, Gemma-3-4B, or Llama-3.2-1B/3B. Qwen3 is neat because it has /think and /no_think: use slower reasoning only when needed. MiMo is worth watching too: Xiaomi's MiMo-7B-RL is on GitHub/HuggingFace, tuned for math, code and reasoning. The paper says the base model used 25T pretraining tokens, then RL on 130K verifiable math/code tasks.
Video: Lightricks/LTX-Video and LTXV-13B can run locally through Python/ComfyUI, but be honest with your laptop. The 13B line wants a serious GPU. For experiments, start with distilled/FP8 or the 2B branch. Lower quality, much faster iteration.
Your docs: local RAG means Chroma or LanceDB, Ollama embeddings like embeddinggemma or qwen3-embedding, then a small LLM. Important detail: use the same embedding model for indexing and search, or the answers will sound smart but miss the source.
Jupyter AI also fits the stack: chat inside JupyterLab, attach files, ask about a notebook or cell, and connect it to local Ollama or vLLM.
⚠️ Hardware note: 16 GB RAM is fine for 1B to 4B quantized models. 32 GB RAM or a discrete GPU makes 7B to 8B much nicer. Long context eats memory fast: Ollama defaults to 4096 tokens, and raising num_ctx hits RAM/VRAM.
Best 2026 laptop stack: small LLM, local embeddings, RAG, Jupyter or IDE integration. You can build it without cloud calls and without a token bill.94 035
🖥 AidLearning
🛠AidLearning is a mobile AI development platform that supports all mainstream development frameworks and tools for deep learning and neural networks.🔰 It has a unique cpu+gpu+npu(dsp) acceleration technology, that brings a significant boost on performance of deep-learning algorithm by the built-in aidlite module. 🔰At the same time, AidLearning also provides developers with popular development tools, such as VSCode and Jupyter Notebook. 🌐 Links: Github
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💡 Welcome to The Premium Vault – Your Gateway to Exclusive Content
🔐 What is The Premium Vault?
We are a private Telegram channel dedicated to delivering high-quality, premium content that you simply cannot find through ordinary searches, free platforms, or standard telegram channels. Every piece of content inside this vault is carefully collected, researched, and created exclusively for our members.📦 What’s Inside? 1⃣ Tutorials, and resources across various premium niches 🔢 Downloadable assets, templates and tools 🔢 Masterpiece Movies and TV Shows 🔢 Legendary Documentaries 🔢 Premium Applications, fully featured, paid-tier software and productivity tools 〰️〰️〰️〰️〰️〰️〰️〰️〰️ 🚫 What You Won't Find Here: No recycled freebies. No low-effort posts. No clickbait. Everything inside The Premium Vault is original, valuable, or rare — shared only with our inner circle of premium subscribers. 🔗 https://t.me/ThePremiumVault/4
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03_AI_Research_and_the_Quest_for_Artificial_General_Intelligence.zip447.31 MB
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🔅 AI for Beginners: Inside Large Language Models
⏲ 3 hours 📁 26 Lessons
📔 Understand how LLMs actually work under the hood from scratch with practical and fun lessons. No prior knowledge required!🎙 Taught by: Scott Kerr 🌟 ZTM Link 📤 Download All Courses
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🔥 Project: fast-agent
fast-agent is a modern framework for rapid development and testing of intelligent agents and workflows supporting the MCP (Model-Context-Protocol) protocol. It provides a simple declarative syntax and powerful tools for building multi-agent systems with support for OpenAI, Anthropic, and other models.
▪️ Main features
• Fast agent creation using @fast.agent decorators, minimizing code amount.
• Workflow support: chains (chain), parallel calls (parallel), routers (router), orchestrators (orchestrator), evaluation and optimization schemes (evaluator_optimizer).
• Multimodality: processing images, PDF files, and integration with external MCP resources.
• Interactive debugging: configuration and diagnostics of agents before, during, and after workflow execution.
• Flexible configuration via fastagent.config.yaml and fastagent.secrets.yaml.
• Integration with LLMs: OpenAI (GPT-4 and others), Anthropic (Haiku, Sonnet, Opus), and other models through MCP servers.
▪️ Quick start
1️⃣ Install the uv package manager for Python.
2️⃣ Install fast-agent:
uv pip install fast-agent-mcp
uv run fast-agent setup
uv run agent.py
uv run fast-agent quickstart workflow
examples directory in the repository.
▪️ Community and development
• The project is actively developed, ⭐️ 1.7k+ stars on GitHub.
• Discussions: [Discussions](https://github.com/evalstate/fast-agent/discussions)
• Latest releases: [Releases](https://github.com/evalstate/fast-agent/releases)
▪️ Video review
[First Look at Fast-Agent (or Manus) – Coding an AI ...](https://www.youtube.com/watch?v=GaVQyYougPc&utm_source=chatgpt.com)
🔍 GitHub94 035
💥 Xiaomi MiMo
🛠Xiaomi Corporation has introduced its first AI model, the compact language model MiMo , which has 7 billion parameters .
🔰It demonstrates performance comparable to GPT o1-mini .
🔰The model code and weights are available on the Hugging Face platform . 🔍
🔰MiMo was trained from scratch in two stages: first for text analysis , and then for solving logic problems , programming and mathematics , using a dataset of 130 thousand tasks . 📊
☝🏻 It is especially worth noting that due to a small number of parameters , MiMo can be run locally . 💻
🔗 Links:
https://github.com/XiaomiMiMo/MiMo
https://huggingface.co/XiaomiMiMo
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🔅 Enhancing Your Notebook Workflow with Jupyter AI
📝 Learn to leverage generative AI capabilities within JupyterLab to enhance your data science and machine learning workflow.
🌐 Author: Wuraola Oyewusi
🔰 Level: Intermediate
⏰ Duration: 1h 10m
📋 Topics: Generative AI Tools, Jupyter, Artificial Intelligence
🔗 Join Artificial intelligence for more courses
94 035
💻 Scrap
🛠 Scraperr is a self-hosted application designed to accurately extract data from websites using XPath selectors.
🔰 It provides a convenient interface for managing scraping tasks, viewing and exporting data.
🔰 Key features include XPath-based extraction, queue management, scraping all pages of a single domain, adding custom headers, automatic media downloading, and visualizing results in tables.
🔰 The application is intended only for sites where scraping is allowed, and the developer is not responsible for possible abuse.
🔗Links:
https://github.com/jaypyles/Scraperr
94 035
💡 Welcome to The Premium Vault – Your Gateway to Exclusive Content
🔐 What is The Premium Vault?
We are a private Telegram channel dedicated to delivering high-quality, premium content that you simply cannot find through ordinary searches, free platforms, or standard telegram channels. Every piece of content inside this vault is carefully collected, researched, and created exclusively for our members.📦 What’s Inside? 1⃣ Tutorials, and resources across various premium niches 🔢 Downloadable assets, templates and tools 🔢 Masterpiece Movies and TV Shows 🔢 Legendary Documentaries 🔢 Premium Applications, fully featured, paid-tier software and productivity tools 〰️〰️〰️〰️〰️〰️〰️〰️〰️ 🚫 What You Won't Find Here: No recycled freebies. No low-effort posts. No clickbait. Everything inside The Premium Vault is original, valuable, or rare — shared only with our inner circle of premium subscribers. 👌 Why Subscribe? Because the best content is never free. By joining The Premium Vault, you gain a competitive edge, save hours of searching, and access movies, documentaries, and applications others wish they knew existed. 🔒 Privacy & Security This is a private, invite-only channel. No bots, no data selling, no distractions. 🔗 https://t.me/ThePremiumVault/4
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💳 Try RedotPay for online payments using cryptocurrencies.
Register with the RedotPay app and receive a $5 welcome bonus upon meeting the offer requirements. You can create a digital or physical card, depending on your needs.✅ Digital Card: Suitable for online payments. ✅ Physical Card: Suitable for deposits and withdrawals at supported ATMs. ✅ Supports uses such as online purchases, subscriptions, advertising, and some international payment services (subject to availability). 💰 Card creation fees: 🖤 Digital Card: $10 ❤️ Physical Card: $100 Important Note: The $5 bonus is for spending only and cannot be used to pay the card creation fee. 🔗 Registration link: https://url.hk/i/en/jzy14
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֎ New: ChatGPT users can now build real apps directly from chat
ChatGPT's app store just added AppDeploy, and it works with free accounts too.
Describe what you want to build, ChatGPT writes the code, AppDeploy handles deployment automatically inside the same chat, and you get a working link back right away.
AppDeploy includes the infrastructure needed for real apps:
🔐 User login and permissions
🗄 Storage, database, realtime sync and notifications
🤖 Built in AI capabilities
☁️ Full backend, background jobs and scheduled tasks
🧪 Automatic QA for every change and built in versioning
🌐 Custom domains, secrets management and more
Free to use. No subscription or credit card required.
👉 Install AppDeploy in ChatGPT and launch your first app in a few minutes
