Body: <p>In deep learning, most of the applications are from text and images. Both text and images can be converted into a tensor of real numbers.</p>
  <p>Other than both mentioned above, there may be some other real-world data used by Deep learning algorithms. Any algorithm in general takes tensor as input and gives tensor as output. As far as I know, tensors always consist of real numbers.</p>
  <p>I want to know whether there are any practical applications that deal with the tensors containing non-real numbers. <em>Is there any such possibility</em>?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-11-19T16:30:54.097Z
Comments.Text: What about a Fourier transform of a time series?
Comments.UserDisplayName: Dave
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-11-29T03:36:41.897Z
Comments.Text: @Dave I will check once.
Comments.UserDisplayName: hanugm
Tags: <deep-learning><datasets><applications><tensor>
Title: Do any practical deep learning algorithms deal with tensors containing non-real entries?
UserDisplayName: hanugm
ThreadText: # Do any practical deep learning algorithms deal with tensors containing non-real entries?
  
  (Asked by: username_0 on 2021-11-19)
  
  In deep learning, most of the applications are from text and images. Both text and images can be converted into a tensor of real numbers.
  
  Other than both mentioned above, there may be some other real-world data used by Deep learning algorithms. Any algorithm in general takes tensor as input and gives tensor as output. As far as I know, tensors always consist of real numbers.
  
  I want to know whether there are any practical applications that deal with the tensors containing non-real numbers. *Is there any such possibility*?
  
  **Tags:** deep-learning, datasets, applications, tensor
  
  > **Comments:**
  > **username_1**: What about a Fourier transform of a time series?  
  > **username_0**: @username_1 I will check once.  
  
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  ## 0 answers
  
  