What is Embedding in AI

In this video, Gyula Rabai Jr, helps you explore the concept of embedding in AI and how it helps machines understand the meaning behind words. If you've ever wondered how AI models can comprehend words like "cat" beyond just a simple label, embedding is the key. Embedding takes a word and transforms it into a list of numbers, or a vector, that encodes its meaning.

Key Topics

  • Word embeddings explained
  • How AI understands word meaning
  • Embedding vectors and their role in AI models
  • The relationship between words and numbers in AI
  • Understanding embeddings in machine learning

Video overview

When we say "cat" we don't just want the model to recognize the word; we want it to understand that a cat is a furry, four-legged creature. To do this, we break down the word "cat" into multiple dimensions, each representing a different aspect of the word's meaning, like fur and legs. These dimensions are represented as numbers in a vector, which helps AI models process and understand the underlying meaning of words.

In this video, we cover:

  • What embedding is and how it works
  • Why AI models need embeddings to understand word meanings
  • How embeddings use vectors to represent words' meanings
  • The analogy of coordinate geometry to explain vectors in embedding
  • Real-world applications of word embeddings in AI and machine learning

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