Mar 3
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The basics of one-hot encoding and its limitations Word embeddings: The secret to AI understanding meaning (Word2Vec, GloVe, LSA) Tokenization and subword efficiency with BPE Transformers: Positional encoding, self-attention, and contextual embeddings (BERT) AI biases and real-world impacts Modern AI stack: Vector databases and Retrieval-Augmented Generation (RAG) What you'll learn (with timings): 0:00 - Intro to AI Fundamentals: Words to numbers 0:36 - One-Hot Encoding (and why it fails) 1:09 - Word Embeddings & Semantic Space 1:42 - Early approaches like Latent Semantic Analysis (LSA) 2:22 - Word2Vec revolution: CBOW vs Skip-Gram 2:58 - Negative Sampling & efficiency tricks 3:32 - GloVe: Global Vectors + famous word analogies 4:08 - Tokens & Subword Tokenization (BPE) 4:38 - Contextual Embeddings: BERT ends static vectors 5:00 - Positional Encoding: How Transformers understand order 6:00 - Biases in Embeddings: Reflecting human history 6:50 - Vector Databases + Retrieval-Augmented Generation (RAG) Perfect for AI beginners, developers, and anyone curious about ChatGPT's foundations. Subscribe for more AI tutorials! #ai #tokenization #embeddings #transformers #genai #ai2026