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Explain embedding in tensorflow?

Answer Posted / Dheeraj Kumar Raisinghani

Embedding is a method used to convert categorical data into a format that can be processed by neural networks. In TensorFlow, an embedding is represented as a lookup table or a parameterized variable that maps each unique category to a dense vector of numbers. This allows the model to capture relationships between categories during learning.

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