Embeddings Explained | Word2Vec, GloVe, BERT & Modern AI

Embeddings are one of the most important concepts in *Natural Language Processing (NLP)* and **Large Language Models (LLMs)**. They transform words, sentences, images, and other data into dense numerical vectors, allowing AI systems to understand meaning, similarity, and relationships. In this video, you'll learn: ✅ What Embeddings are ✅ Why AI needs vector representations ✅ Bag of Words (BoW) explained ✅ TF-IDF explained ✅ The Distributional Hypothesis ✅ Word2Vec (CBOW & Skip-Gram) ✅ GloVe embeddings explained ✅ fastText and subword embeddings ✅ Vector arithmetic and semantic relationships ✅ Static vs Contextual Embeddings ✅ Polysemy and word ambiguity ✅ How BERT creates contextual embeddings ✅ Why embeddings power modern LLMs, RAG systems, and semantic search Whether you're an AI Engineer, NLP Researcher, Machine Learning Engineer, Data Scientist, Student, or Generative AI enthusiast, this video provides a complete understanding of one of the foundational building blocks of modern AI. Topics Covered: • Embeddings • Word Embeddings • Word2Vec • GloVe • fastText • TF-IDF • Bag of Words • BERT • Contextual Embeddings • Semantic Search • Large Language Models (LLMs) • Natural Language Processing • Artificial Intelligence Discover how embeddings transformed AI from simple keyword matching to understanding the semantic meaning of language, enabling technologies like ChatGPT, Retrieval-Augmented Generation (RAG), recommendation systems, and intelligent search. 🔔 Subscribe for more videos on Large Language Models, NLP, Machine Learning, AI Engineering, Deep Learning, RAG, Vector Databases, and Generative AI. #Embeddings #Word2Vec #BERT #NLP #LLM #ArtificialIntelligence #MachineLearning #GloVe #FastText #SemanticSearch #RAG #VectorDatabase #AIEngineering #DeepLearning #GenerativeAI Timestamps: 00:00 Introduction 02:00 What Are Embeddings? 07:10 Bag of Words & TF-IDF 13:30 Distributional Hypothesis 19:20 Word2Vec (CBOW & Skip-Gram) 27:10 GloVe Explained 34:00 fastText & Subword Embeddings 40:20 Static vs Contextual Embeddings 47:10 BERT Embeddings 53:30 Applications in LLMs, RAG & Semantic Search 59:30 Key Takeaways