DEFINITION
Word embedding is a natural language processing (NLP) technique that converts words and text into numbers. This lets a machine learning algorithm understand the context of words. Word vectors show the relationships between words based on their meaning.
Why use it?
- Text can be converted into numerical form.
- AI models can understand the relationships between words.
- Text can be categorized more effectively.
- Information can be retrieved based on similarity.
When to use it?
- Building a semantic search engine.
- Chatbots and virtual assistants.
- Product recommendations.
- Sentiment analysis.
- Categorizing documents and clustering text.
See it running on your own hardware
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