Hands-On Deep Learning with R

Exploring embeddings

Broadly speaking, deep neural networks seek to minimize the loss (error) associated with non-linear data representations used for learning important features from input data.

In addition to traditional dimensionality reduction methods such as clustering and KNN or matrix factorization (PCA, clustering, and other probabilistic techniques), recommender systems can use neural network embeddings to support dimensionality reduction and distributed, non-linear data representations in scalable and efficient ways.

Embeddings are low-dimensional representations (vectors) of continuous numbers learned from representations (vectors) of discrete input variables in neural networks.

Neural network embeddings offer several advantages ...