Body: <p>I need to predict the performance (CPI cycles-per-instruction) of 90 machines for the next hour (or day). Each machine has a thousand records (e.g. CPU and memory usage). </p>
  
  <p>Currently, I am using a neural network with one hidden layer for this task. I have 9 inputs (features), 23 neurons in the hidden layer, and one output. I am using the Levenberg-Marquardt algorithm. Examples of the inputs (or features) are the CPU and memory capacity and usage, and the <code>machine_id</code>. and Output is performance. I have 90 machines. Currently, I get an MSE of <span class="math-container">$0.1$</span> and an R of <span class="math-container">$0.80$</span>.</p>
  
  <p>My dataset consist of 30 days. I trained my network for the first 29 days, and I use day 30 to test. </p>
  
  <p>I have been advised to use deep learning to have more flexibility and improve the MSE and R results. Could deep learning be helpful in this case? If yes, which deep learning model could I use to improve the results?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-12-12T07:10:42.020Z
Comments.Text: No. Deep Learning efficient for big networks and samples of extremely high dimensions. It don't make sense to use it for   9 element vectors as samples.
Comments.UserDisplayName: mirror2image
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-12-12T07:30:12.023Z
Comments.Text: It's highly plausible that you don't need anything neural. For model selection, the following image is always a good rule of thumb. [https://i.sstatic.net/Y8jAg.png](https://i.sstatic.net/Y8jAg.png).
Comments.UserDisplayName: MatthewTuby
Tags: <neural-networks><deep-learning><deep-neural-networks>
Title: Should I use deep learning to solve my task?
UserDisplayName: jou
ThreadText: # Should I use deep learning to solve my task?
  
  (Asked by: username_0 on 2019-12-09)
  
  I need to predict the performance (CPI cycles-per-instruction) of 90 machines for the next hour (or day). Each machine has a thousand records (e.g. CPU and memory usage).
  
  Currently, I am using a neural network with one hidden layer for this task. I have 9 inputs (features), 23 neurons in the hidden layer, and one output. I am using the Levenberg-Marquardt algorithm. Examples of the inputs (or features) are the CPU and memory capacity and usage, and the `machine_id`. and Output is performance. I have 90 machines. Currently, I get an MSE of $0.1$ and an R of $0.80$.
  
  My dataset consist of 30 days. I trained my network for the first 29 days, and I use day 30 to test.
  
  I have been advised to use deep learning to have more flexibility and improve the MSE and R results. Could deep learning be helpful in this case? If yes, which deep learning model could I use to improve the results?
  
  **Tags:** neural-networks, deep-learning, deep-neural-networks
  
  > **Comments:**
  > **username_1**: No. Deep Learning efficient for big networks and samples of extremely high dimensions. It don't make sense to use it for   9 element vectors as samples.  
  > **username_2**: It's highly plausible that you don't need anything neural. For model selection, the following image is always a good rule of thumb. [https://i.sstatic.net/Y8jAg.png](https://i.sstatic.net/Y8jAg.png).  
  
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  ## 0 answers
  
  