2 Vectors, matrices, and tensors in machine learning
This chapter covers
- Vectors and matrices and their role in datascience
- Working with eigenvalues and eigenvectors
- Finding the axes of a hyper-ellipse
At its core, machine learning, and indeed all computer software, is about number crunching. We input a set of numbers into the machine and get back a different set of numbers as output. However, this cannot be done randomly. It is important to organize these numbers appropriately and group them into meaningful objects that go into and come out of the machine. This is where vectors and matrices come in. These are concepts that mathematicians have been using for centuries—we are simply reusing them in machine learning.
In this chapter, we will ...
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