embedding layers
Embedding layers are a fundamental component of deep learning models used for processing textual or categorical data. They convert discrete inputs (such as words or categories) into continuous vector representations with the aim of capturing meaningful relationships and similarities between different inputs. These vector representations, called embeddings, enable the model to better understand and generalize patterns in the data, aiding in various Natural Language Processing (NLP) tasks like sentiment analysis, language translation, and text generation.
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