Understanding embeddings
At its core, an embedding is a mapping from a high-dimensional space to a lower-dimensional space that captures essential characteristics or features of data in a more compact form. This transformation not only reduces the dimensionality of the data but also helps NNs process and understand it more effectively.
These compact, meaningful representations of data play a pivotal role in various applications, from NLP to recommendation systems. In this section, we’ll explore the concept of embeddings, their significance, and how they are employed to enhance the capabilities of NNs.
Word embeddings
Word embeddings are among the most renowned and widely used types of embeddings. They represent words as vectors in a continuous space, where each dimension of the vector corresponds to a semantic or syntactic feature of the word. This representation enables NNs to grasp meanings and relationships between words more intuitively.
Word embedding models...
