Embeddings Tutorial 🌐: Word2Vec Hack – Numbers that Grok Meaning
Dive into embeddings guide – dense vectors for words that capture semantics. Key for LLM understanding.
What Are Embeddings?
- Dense vectors representing words or tokens.
- Similar concepts cluster close in vector space.
Types of Embeddings
- Static: Word2Vec guide, fixed representations.
- Contextual: BERT-style, dynamic based on sentence.
- Sentence-level: Vectors for entire texts.
Training Embeddings
- Skip-gram: Predict surrounding words.
- CBOW: Predict center word from context.
- GloVe: Leverage co-occurrence matrices.
Why Embeddings Rock in AI
Transform text to math! How do you use embeddings in your projects? Tell me! 🔍
My Embeddings Notes
Top Embeddings Resources
- Word2Vec Paper
- GloVe Paper
- FastText
- Colah’s MNIST Visualization Post
- Hugging Face Embeddings Guide
- OpenAI Embeddings
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