Answers.Body: <p><a href="https://ai.stackexchange.com/q/11226/2444">Graphs</a>.</p>
  <p>In <a href="https://en.wikipedia.org/wiki/Graph_(discrete_mathematics)" rel="nofollow noreferrer">graphs</a>, nodes and edges do not <em>usually</em> have a specified position/orientation: for example, we cannot say that node <span class="math-container">$A$</span> is to the right/left of node <span class="math-container">$B$</span> because edges do not typically have an orientation. In grid-like structures, like images, we could say that about pixels. In a graph, all we know is that nodes are connected to other nodes via some edges, which may have weights, but, again, no orientation.</p>
  
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Answers.CreationDate: 2021-08-14T11:01:43.957Z
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Body: <p>I am reading this book called &quot;<a href="https://www.deeplearningbook.org/" rel="nofollow noreferrer">Deep Learning</a>&quot; (by Goodfellow, Bengio and Courville).</p>
  <p><a href="https://www.deeplearningbook.org/contents/convnets.html" rel="nofollow noreferrer">On page 326</a>, in the first paragraph, it says:</p>
  <blockquote>
  <p>CNNs, are a specialized kind of neural network for processing data that has a known grid-like topology. Examples include time-series data, which can be thought of as a 1-D grid taking samples at regular time intervals, and image data, which can be thought of as a 2-D grid of pixel</p>
  </blockquote>
  <p>Considering an image as a grid is completely intuitive. And, similarly, we can extend the logic to a 1-D time series.</p>
  <p>But then what cannot be considered as having a grid-like structure?</p>
  
Tags: <deep-learning><terminology><geometric-deep-learning><books><non-euclidean-data>
Title: What exactly is a grid-like topology according to the book Deep Learning?
UserDisplayName: Aether
ThreadText: # What exactly is a grid-like topology according to the book Deep Learning?
  
  (Asked by: username_0 on 2021-08-14)
  
  I am reading this book called "[Deep Learning](https://www.deeplearningbook.org/)" (by Goodfellow, Bengio and Courville).
  
  [On page 326](https://www.deeplearningbook.org/contents/convnets.html), in the first paragraph, it says:
  
  > CNNs, are a specialized kind of neural network for processing data that has a known grid-like topology. Examples include time-series data, which can be thought of as a 1-D grid taking samples at regular time intervals, and image data, which can be thought of as a 2-D grid of pixel
  
  Considering an image as a grid is completely intuitive. And, similarly, we can extend the logic to a 1-D time series.
  
  But then what cannot be considered as having a grid-like structure?
  
  **Tags:** deep-learning, terminology, geometric-deep-learning, books, non-euclidean-data
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2021-08-14. Score: 1 | Accepted answer
  
  [Graphs](https://ai.stackexchange.com/q/11226/2444).
  
  In [graphs](https://en.wikipedia.org/wiki/Graph_(discrete_mathematics)), nodes and edges do not *usually* have a specified position/orientation: for example, we cannot say that node $A$ is to the right/left of node $B$ because edges do not typically have an orientation. In grid-like structures, like images, we could say that about pixels. In a graph, all we know is that nodes are connected to other nodes via some edges, which may have weights, but, again, no orientation.
  
  
  ---
  
  