Software Engineer | ... • 1y
💡 5 Things You Need to Master for learn for integrating AI into your project 1️⃣ Retrieval-Augmented Generation (RAG): Combine search with AI for precise and context-aware outputs. 2️⃣ Vector Databases: Learn how to store and query embeddings for efficient semantic search. 3️⃣ Hugging Face Ecosystem: Master pre-trained models and tools to accelerate your AI projects. 4️⃣ Model Fine-Tuning: Adapt models to specific tasks for better performance and accuracy. 5️⃣ Client-Side Models: Build lightweight, on-device AI solutions for fast and private processing.
Hey I am on Medial • 7m
I'm developing a smart AI-powered meal planner mobile app that helps users create personalized meal plans based on their health goals, dietary preferences, regional cuisine, and lifestyle. The app suggests meals for breakfast, lunch, snacks, and dinn
See MoreGenai intern • 4m
Over the last few weeks, I had the opportunity to intern at Value Health, where I dived deep into the world of Generative AI and semantic understanding for healthcare. From exploring contextual embeddings and knowledge graphs to optimizing retrieval
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Turning dreams into ... • 12m
India should focus on fine-tuning existing AI models and building applications rather than investing heavily in foundational models or AI chips, says Groq CEO Jonathan Ross. Is this the right strategy for India to lead in AI innovation? Thoughts?
| Technologist | ML ... • 11m
In the ever-evolving AI landscape, a new player is making waves — Deepseek. While OpenAI, Google DeepMind, and Meta AI have been dominant forces, Deepseek is emerging as a formidable contender in the AI race.The recent buzz around Deepseek stems from
See MoreTweakBuzz makes you ... • 7m
AI SEO Tools Guide to Skyrocket Your Google AI-Powered SERP Rankings In today’s rapidly evolving digital world, AI SEO tools have become essential for marketers and businesses aiming to dominate Google’s AI-powered SERP rankings. Traditional SEO str
See MoreAI Deep Explorer | f... • 10m
"A Survey on Post-Training of Large Language Models" This paper systematically categorizes post-training into five major paradigms: 1. Fine-Tuning 2. Alignment 3. Reasoning Enhancement 4. Efficiency Optimization 5. Integration & Adaptation 1️⃣ Fin
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