In the context of neural networks, embeddings are low-dimensional, learned continuous vector representations of discrete variables. Neural network embeddings are useful because they can reduce the dimensionality of categorical variables and meaningfully represent categories in the transformed space.
One notably successful use of deep learning is embedding, a method used to represent discrete variables as continuous vectors. This technique has found practical applications with word embeddings for machine translation and entity embeddings for categorical variables.
Neural network embeddings have 3 primary purposes:
Finding nearest neighbors in the embedding space. These can be used to make recommendations based on user interests or cluster categories.
As input to a machine learning model for a supervised task.
For visualization of concepts and relations between categories.
This means in terms of the book project, using neural network embeddings, we can take all 37,000 book articles on Wikipedia and represent each one using only 50 numbers in a vector. Moreover, because embeddings are learned, books that are more similar in the context of our learning problem are closer to one another in the embedding space
references:
https://towardsdatascience.com/neural-network-embeddings-explained-4d028e6f0526
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