Array Representation Of A General Tensor Question

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SUMMARY

The discussion centers on the representation of tensors, specifically addressing the differences between 2D and 3D array representations. Eigenchris's video series "Tensors for Beginners" emphasizes that while some prefer visualizing tensors as 3D cubes, this approach can obscure critical information regarding the tensor's structure. The key takeaway is that a (m,n) tensor can be effectively represented in a 2D format, allowing for clearer identification of its vector and covector components. The points raised about the limitations of 3D representations are confirmed as accurate.

PREREQUISITES
  • Understanding of tensor notation, specifically (m,n) tensor types
  • Familiarity with basic linear algebra concepts
  • Knowledge of array representations in mathematics
  • Exposure to tensor operations and their applications
NEXT STEPS
  • Explore the properties of (1,2) tensors and their applications in physics
  • Learn about tensor products and their representations in various dimensions
  • Study the differences between vector spaces and covector spaces
  • Investigate the implications of tensor representation in machine learning frameworks
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This discussion is beneficial for students in mathematics or physics, particularly those studying tensors, as well as educators and anyone interested in the foundational concepts of tensor representation and its implications in various fields.

Vanilla Gorilla
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TL;DR
My question is just if the points are correct and true statements; if not, how could I rewrite them? :)
Some people like to write tensor products as 3d arrays, but that inherently means we lose information when compared to the 2D representation
That’s because in the 2D representation, we see this is a (1,2)-tensor, because there's 1 column aspect and 2 row aspects.
A (m,n) tensor can be represented by m column aspects and n row aspects, when converted to array form.
So, I've been watching eigenchris's video series "Tensors for Beginners" on YouTube. I am currently on video 14. I, in the position of a complete beginner, am taking notes on it, and I just wanted to make sure I wasn't misinterpreting anything.

At about 5:50, he states that "The array for Q is a row of rows of columns. And some people like to think that since there are three parts in this tensor, they think that we should visualize this tensor array instead as a 3d cube, like over here. But I don't like to do that. Because when we visualize it this way, we lose out on how many vector parts and how many covector parts there are. And we sort of lose information about what type of tensor This is. But when I write the tensor out like this as a row of rows of columns, I can still see by looking at this, that this is a (1,2)-tensor, because there's one column aspect and two row aspects."
Regarding this, I interpreted it to imply the points below.
My question is just if the points are correct and true statements; if not, how could I rewrite them? :)
  1. Some people like to write tensor products as 3d arrays, but that inherently means we lose information when compared to the 2D representation
  2. A (m,n) tensor can be represented by m column aspects and n row aspects, when converted to array form.
Any help is much appreciated!
P.S., I'm not always great at articulating my thoughts, so my apologies if this question isn't clear.
P.P.S., I know this isn't high school material, but I am currently in high school, which is why I made my level "Basic/high school level"
 
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The points are correct. A (1,2) tensor, a (2,1) tensor, a (3,0) tensor and a (0,3) tensor are all different, but can all be mapped to a 3D block of numbers. Having just a 3D block of numbers doesn't tell you which of those four types of tensor it represents. So it has lost that information about the tensor.
 
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Thank you! Your response is much appreciated! :)
 

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