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AI & Machine Learning & Deep Learning

AI & Machine Learning & Deep Learning

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Here you can Learn and Download 1. Artificial Intelligence 2. Machine Learning 3. Deep Learning 4. NLP 5. Statistics 6. Data Visualization 7. Data Analysis 8. Time Series Analysis Learn Step by Step Machine Learning: https://t.me/LearnAIMLStepbyStep

Ko'proq ko'rsatish
Mamlakat belgilanmaganToif belgilanmagan

📈 Telegram kanali AI & Machine Learning & Deep Learning analitikasi

AI & Machine Learning & Deep Learning (@aimldeepthaught) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 13 115 obunachidan iborat bo'lib, Boshqa toifasida -o'rinni egallagan.

📊 Auditoriya ko‘rsatkichlari va dinamika

невідомо sanasidan buyon loyiha tez o‘sib, 13 115 obunachiga ega bo‘ldi.

23 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 169 ga, so‘nggi 24 soatda esa 9 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.

  • Tasdiqlash holati: Tasdiqlanmagan
  • Jalb etish (ER): Auditoriya o‘rtacha 19.58% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining N/A% ini tashkil etuvchi reaksiyalarni to‘playdi.
  • Post qamrovi: Har bir post o‘rtacha 2 566 marta ko‘riladi; birinchi sutkada odatda 0 ta ko‘rish yig‘iladi.
  • Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 10 ta reaksiya keladi.
  • Tematik yo‘nalishlar: Kontent learning, algorithm, llm, llamaindex, pattern kabi asosiy mavzularga jamlangan.

📝 Tavsif va kontent siyosati

Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
Here you can Learn and Download 1. Artificial Intelligence 2. Machine Learning 3. Deep Learning 4. NLP 5. Statistics 6. Data Visualization 7. Data Analysis 8. Time Series Analysis Learn Step by Step Machine Learning: https://t.me/LearnAIMLStepbyS...

Yuqori yangilanish chastotasi (oxirgi ma’lumot 24 Iyun, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Boshqa toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.

13 115
Obunachilar
+924 soatlar
+317 kunlar
+16930 kunlar
Postlar arxiv
Machine Learning Platform Engineer

Machine Learning Platform Engineer
Machine Learning Platform Engineer

Build a Reasoning Model

Build a Reasoning Model
Build a Reasoning Model

Machine Learning with Python Cookbook Follow this Instagram channel to learn the latest in the AI world: https://www.instagram.com/neural_nexus_ai_?igsh=bTdhNzNuMHI4YWFz

Machine Learning with Python Cookbook
Machine Learning with Python Cookbook

Generative AI on AWS

Generative AI on AWS
Generative AI on AWS

Low Cost AI
Low Cost AI

🚀 Understanding the AI Context Window — The Brain Behind AI Coding Assistants Today’s AI coding tools like Claude Code, ChatGPT, Cursor, and Copilot work using something called a Context Window. Think of it as the AI’s working memory while solving problems, writing code, debugging, or building projects. The image below explains how this memory is divided internally inside advanced AI systems. 🔍 Main Segments of the Context Window 🟣 System Prompt Core instructions that control AI behavior, safety, and rules. 🟦 Tool Schemas Definitions of tools like terminal, file reader, search, Git, etc. 🟢 CLAUDE.md / Project Memory Persistent project instructions, coding standards, and architecture notes. 🟧 Conversation History Your prompts + AI replies. This becomes the biggest memory consumer in long sessions. 🟥 Tool Results Terminal logs, build outputs, stack traces, grep results, file outputs. One of the hidden reasons why AI memory fills quickly. 🔵 Skills + MCP External capabilities and integrations loaded during startup. ⚪️ Auto Compact Buffer Reserved memory used for automatic summarization and compression. ⚫️ Free Space Remaining usable memory for reasoning, prompts, and new files. 💡 Why This Is Important As AI adoption increases in: Software Engineering Data Science Finance Healthcare Research Education Understanding AI memory systems becomes very important. A larger and cleaner context window means: ✅ Better reasoning ✅ Better code generation ✅ Less hallucination ✅ Improved debugging ✅ More consistent AI behavior ✅ Better handling of large-scale projects 🧠 Real-World Use Cases ✔️ Large Software Development Projects ✔️ AI Agents & Autonomous Systems ✔️ Multi-file Code Understanding ✔️ Enterprise AI Assistants ✔️ Research Automation ✔️ AI-Powered Education Systems ✔️ Data Analytics & ML Workflows 📈 Why Developers Should Learn This Most developers focus only on prompts. But professional AI engineering now requires understanding: Token management Memory optimization Context engineering AI workflow design MCP integrations Prompt architecture This is becoming a core future skill in AI Engineering. 🔥 The bigger the AI project, the faster the context window fills. Managing context efficiently is now becoming a real engineering skill.

Context Window
Context Window

Practical Statistics for Data Scientists
Practical Statistics for Data Scientists

AI Engineering

AI Engineering
AI Engineering

Generative AI with LangChain

Generative AI with LangChain
Generative AI with LangChain

Deep Learning for the Life Sciences