Explain what an embedding layer is and its purpose in Machine Learning.
An embedding layer is a layer in a machine learning model that maps inputs to a lower-dimensional space. Its purpose is to make it easier for the model to process certain types of data by transforming it into a more suitable form. For example, in a word embedding layer, words are converted into vectors that capture the semantic meaning of the words. This layer can then be used as an input to the neural network, allowing it to process the textual data with greater ease.
An embedding layer is a type of layer in a neural network that is used to transform input data from one space to another. It is typically used in the context of word embedding, where words are converted into numerical vectors that can be used by other layers in the network. The purpose of the embedding layer is to provide a common representation of the data that can be used by the rest of the network, allowing for more effective and efficient learning.