midKeras
What is embedding layer in Keras?
Updated May 16, 2026
Short answer
Embedding layer converts categorical data into dense vectors.
Deep explanation
It maps discrete tokens into continuous vector spaces capturing semantic relationships.
Real-world example
Used in NLP sentiment analysis models.
Common mistakes
- Using embeddings without tokenization.
Follow-up questions
- What is embedding dimension?
- Why embeddings instead of one-hot?