Welcome back to Machine Learning Guide. I'm your host, Tyler Rinelli.

MLG teaches the fundamentals of machine learning and artificial intelligence.

It covers intuition, models, math, languages, frameworks, and more.

Where your other machine learning resources provide the trees, I provide the forest.

Visual is the best primary learning modality, but audio is a great supplement during exercise commute and chores.

Consider MLG your syllabus with highly curated resources for each episode's details at OCdevel.com forward slash MLG.

I'm also starting a new podcast which could use your support. It's called

Lefnear's Life Hacks and teaches productivity focused tips and tricks, some which could prove

beneficial in your machine learning education journey. Find that at Ocdevel.com forward slash

LLH. You're listening to Machine Learning Applied. In this episode, we're going to talk about

shapes and sizes of ND arrays and tensors. I personally found shaping to be a very confusing

concept when I first started doing machine learning. It's something you definitely don't deal with

outside of machine learning and data science and regular computer programming and web development and the sort. So it took me a while to

get comfortable with it and it just takes practice. So you'll eventually get it when working in

machine learning. But I figured I'd do an episode and give you a lay of land. So as a recap on something

I've mentioned multiple times in MLG, when you're dealing with arrays of multiple dimensions, we call that a tensor.

So an array like you're used to, a standard array, we call that a 1D tensor, a one-dimensional tensor, or sometimes a vector.

A two-dimensional array, or 2D tensor, is called a vector. A two-dimensional array or 2D tensor is called a matrix.

Incidentally, a 0D tensor is called a scalar, which is just a number, like the number 5 or the number 10.

That's a 0D tensor. So the general term we have for any dimensional array is a tensor

in mathematics. In NumPi, they call it an n-d array, any dimensional array. And we have names

for 0d, 1d, and 2D tensors, that is, scalar, vector, and matrix, respectively.

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