AI Embeddings Explained - What They Are & Why They Matter | AI Engineering Tutorial

Description

Learn what AI embeddings are and why they're fundamental to modern AI applications! This tutorial breaks down embeddings from basics to real-world use cases. 🎯 What You'll Learn: ✅ What are embeddings and how they work ✅ Converting text to numbers (vector representations) ✅ Understanding similarity and semantic meaning ✅ The math behind embeddings (cosine similarity) ✅ Why 384, 768, or 1536 dimensions? ✅ Real-world applications (RAG, semantic search, recommendations) 📚 Perfect For: • Software engineers learning AI • Developers building AI applications • Anyone working with LLMs and vector databases • Engineers implementing RAG systems ⏱️ Timestamps: 0:00 - Introduction 0:15 - What Are Embeddings? 2:30 - Text to Numbers 4:45 - Similar Meanings 6:20 - The Math Behind It 8:10 - Why 384 Dimensions? 9:40 - Real-World Applications 🔗 Resources: 📂 GitHub: https://github.com/code-to-innovation 💻 Code examples and notebooks included 🎓 Part of the AI Engineering series - watch the full playlist for complete learning path! 👍 Like and Subscribe for more AI engineering tutorials! #AIEmbeddings #MachineLearning #AIEngineering #VectorDatabase #RAG #SemanticSearch #AI #LLM #DeepLearning #Tutorial