Learn AI

🚀 *We’re Hiring: Data Analyst Trainer | Udaipur* 📊 Are you passionate about data analysis and excited to share your expertise with future professionals? We are looking for a *Data Analyst Trainer* to join our team in *Udaipur, Rajasthan*! If you have practical experience in *Python*, *Power BI*, and *Advanced Excel*, along with a flair for teaching and mentoring, we’d love to hear from you! 🔍 *Position*: Data Analyst Trainer 📍 *Location*: Udaipur, Rajasthan 🕒 *Experience*: Minimum 1 year of training experience 💰 *Salary*: ₹3 LPA to ₹5.4 LPA *Qualifications*: ✅ Degree or Diploma in *Computer Science*, *IT*, or related fields ✅ Strong hands-on knowledge of *Python*, *Power BI*, and *Advanced Excel* ✅ Ability to simplify and communicate complex data analysis concepts effectively *Skills*: 💡 Excellent *teaching* & *presentation* skills 💡 In-depth knowledge of *Python*, *Power BI*, and *Excel* 💡 Ability to engage and inspire students to master data analysis tools and techniques 🚀 Be a part of our mission to shape the next generation of data professionals! 📩 *Interested?* Send your CV to *akash@certedtechnologies.com* or contact *7748888320* for more information. Let's make data analysis fun and engaging together! 📊✨ @Artificial_intelligence_ai https://t.me/Artificial_intelligence_AI

This free 8 hour course from NVIDIA is all you need to start building RAG Agents with LLMs It talks in depth about - - LLM Inference Interfaces - Pipeline Design with LangChain - Gradio and LangServe - Dialog Management with Running States - Working with Documents - Embeddings for Semantic Similarity and Guardrailing - Vector Stores for RAG Agents Start your course here - https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-FX-15+V1

Guide to Building an AI Agent 1️⃣ 𝗖𝗵𝗼𝗼𝘀𝗲 𝘁𝗵𝗲 𝗥𝗶𝗴𝗵𝘁 𝗟𝗟𝗠 Not all LLMs are equal. Pick one that: - Excels in reasoning benchmarks - Supports chain-of-thought (CoT) prompting - Delivers consistent responses 📌 Tip: Experiment with models & fine-tune prompts to enhance reasoning. 2️⃣ 𝗗𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁’𝘀 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝗟𝗼𝗴𝗶𝗰 Your agent needs a strategy: - Tool Use: Call tools when needed; otherwise, respond directly. - Basic Reflection: Generate, critique, and refine responses. - ReAct: Plan, execute, observe, and iterate. - Plan-then-Execute: Outline all steps first, then execute. 📌 Choosing the right approach improves reasoning & reliability. 3️⃣ 𝗗𝗲𝗳𝗶𝗻𝗲 𝗖𝗼𝗿𝗲 𝗜𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻𝘀 & 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀 Set operational rules: - How to handle unclear queries? (Ask clarifying questions) - When to use external tools? - Formatting rules? (Markdown, JSON, etc.) - Interaction style? 📌 Clear system prompts shape agent behavior. 4️⃣ 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗮 𝗠𝗲𝗺𝗼𝗿𝘆 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 LLMs forget past interactions. Memory strategies: - Sliding Window: Retain recent turns, discard old ones. - Summarized Memory: Condense key points for recall. - Long-Term Memory: Store user preferences for personalization. 📌 Example: A financial AI recalls risk tolerance from past chats. 5️⃣ 𝗘𝗾𝘂𝗶𝗽 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁 𝘄𝗶𝘁𝗵 𝗧𝗼𝗼𝗹𝘀 & 𝗔𝗣𝗜𝘀 Extend capabilities with external tools: - Name: Clear, intuitive (e.g., "StockPriceRetriever") - Description: What does it do? - Schemas: Define input/output formats - Error Handling: How to manage failures? 📌 Example: A support AI retrieves order details via CRM API. 6️⃣ 𝗗𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁’𝘀 𝗥𝗼𝗹𝗲 & 𝗞𝗲𝘆 𝗧𝗮𝘀𝗸𝘀 Narrowly defined agents perform better. Clarify: - Mission: (e.g., "I analyze datasets for insights.") - Key Tasks: (Summarizing, visualizing, analyzing) - Limitations: ("I don’t offer legal advice.") 📌 Example: A financial AI focuses on finance, not general knowledge. 7️⃣ 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 𝗥𝗮𝘄 𝗟𝗟𝗠 𝗢𝘂𝘁𝗽𝘂𝘁𝘀 Post-process responses for structure & accuracy: - Convert AI output to structured formats (JSON, tables) - Validate correctness before user delivery - Ensure correct tool execution 📌 Example: A financial AI converts extracted data into JSON. 8️⃣ 𝗦𝗰𝗮𝗹𝗶𝗻𝗴 𝘁𝗼 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 (𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱) For complex workflows: - Info Sharing: What context is passed between agents? - Error Handling: What if one agent fails? - State Management: How to pause/resume tasks? 📌 Example: 1️⃣ One agent fetches data 2️⃣ Another summarizes 3️⃣ A third generates a report Master the fundamentals, experiment, and refine and.. now go build something amazing! (Written by : Armand Ruiz) . . . . . Only playlist you need to look to learn Machine Learning from Basics https://youtube.com/playlist?list=PL9m8ngZLLVomZCPblj4Py7HpQapy5dlfB&si=5p0auz1fYVzikpJr @Artificial_intelligence_ai https://t.me/Artificial_intelligence_AI

DeepMind in collaboration with University College London "Reinforcement Learning Lecture Series 2021" Website: https://lnkd.in/gwykwSAy Video lectures: https://lnkd.in/gJxaQXic 👉@Artificial_intelligence_ai Telegram: https://t.me/Artificial_intelligence_AI

Are They the Same? Exploring Visual Correspondence Shortcomings of Multimodal LLMs 🖥 Github: https://github.com/zhouyiks/CoLVA/tree/main 📕 Paper: https://arxiv.org/pdf/2501.04670v1.pdf ⭐️ Dataset: https://paperswithcode.com/dataset/bdd100k 👉@Artificial_intelligence_ai Telegram: https://t.me/Artificial_intelligence_AI