Embeddings in Machine Learning: Making Complex Data Simple

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https://www.toptal.com/machine-learning/embeddings-in-machine-learning

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Notes

• "An embedding is a low-dimensional representation of high-dimensional data. Typically, an embedding won’t capture all information contained in the original data. A good embedding, however, will capture enough to solve the problem at hand."
• "There exist many embeddings tailored for a particular data structure. For example, you might have heard of word2vec for text data, or Fourier descriptors for shape image data."
• "Principal Component Analysis, or PCA, is probably the most widely used embedding to date."