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These sound scary, but represent an intuitive concept: Finding the core directions of data.
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Matrix Decomposition (or Factorization) is simply breaking a big, complicated matrix down into smaller, simpler pieces.
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If you've ever seen an AI paper, you've probably seen $W \times X$. Matrix multiplication is the absolute workhorse of deep learning.
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Welcome to the foundational building blocks of all Machine Learning data! Don't let the math terms scare you—think of these simply as different types of containers for our data.