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in the early days of neural networks working with categorical data presented a docHub challenge neural networks require numerical inputs but categorical data such as words are discrete in nature thatamp;#39;s why it couldnamp;#39;t be directly processed by neural network we needed a solution that could translate categorical data into numerical representations one solution was to encode it using one hot encoding but that too fails when number of words increases and unable to capture relationship between words because the encoding is discrete in nature for large corpuses as well as capturing semantic of sentences we need a continuous numerical representation of categorical data and thatamp;#39;s where word embedding come to rescue an embedding layer is essentially a mapping between discrete categories and continuous Vector in higher dimensional space the goal of the embedding layer is to arrange the vector in a way that reflects the relationship between categories similar categor
