Body: <p>I am a beginner in machine learning and neural networks. I have only used neural networks for classification problems.  My aim is to modify it so that it can work for polynomial regression as well. In my problem, I have three inputs and three outputs. My aim is to predict these three outputs based on these three inputs. The outputs are real-valued, and can take positive and negative values. </p>
  
  <p>How should I choose the activation functions? I have only used sigmoid. </p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-05-09T17:50:39.580Z
Comments.Text: For regression problems we use linear activation.
Comments.UserDisplayName: desertnaut
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-05-10T02:52:44.383Z
Comments.Text: Try the popular ReLU activation function (or its variant, the leaky ReLU)
Comments.UserDisplayName: stoic-santiago
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-05-10T14:15:30.153Z
Comments.Text: Thank you. I have one more doubt. The three input data are in the range of 100s eg(500,600,700) and the output have mixed units (700, -0.52, 0.45). Should I normalize the input as well as output ?
Comments.UserDisplayName: isaac john
Tags: <neural-networks><machine-learning><ai-design><activation-functions><regression>
Title: Which activation functions should I use for polynomial regression?
UserDisplayName: isaac john
ThreadText: # Which activation functions should I use for polynomial regression?
  
  (Asked by: username_0 on 2020-05-09)
  
  I am a beginner in machine learning and neural networks. I have only used neural networks for classification problems. My aim is to modify it so that it can work for polynomial regression as well. In my problem, I have three inputs and three outputs. My aim is to predict these three outputs based on these three inputs. The outputs are real-valued, and can take positive and negative values.
  
  How should I choose the activation functions? I have only used sigmoid.
  
  **Tags:** neural-networks, machine-learning, ai-design, activation-functions, regression
  
  > **Comments:**
  > **username_1**: For regression problems we use linear activation.  
  > **username_2**: Try the popular ReLU activation function (or its variant, the leaky ReLU)  
  > **username_0**: Thank you. I have one more doubt. The three input data are in the range of 100s eg(500,600,700) and the output have mixed units (700, -0.52, 0.45). Should I normalize the input as well as output ?  
  
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  ## 0 answers
  
  