Explain embedding in tensorflow?
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Answer / 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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