Embedding Layer
In deep learning, an embedding layer is a neural network layer that maps categorical variables, such as words or IDs, to continuous vectors of fixed size. The goal of the embedding layer is to learn a representation of the input data that captures the underlying relationships and similarities between the different categories. This is achieved by training the neural network to predict the target variable based on the input data and adjusting the weights of the embedding layer during the backpropagation process. The resulting embeddings can then be used as input to other layers in the neural network, such as convolutional or recurrent layers, to perform tasks such as image or text classification.
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