Answers.Body: <blockquote>
  <p>It seems like transfer learning is only applicable to neural networks. Is this a correct assumption?</p>
  </blockquote>
  <p>No. <a href="https://en.wikipedia.org/wiki/Transfer_learning" rel="nofollow noreferrer">Wiki</a> page give you pointers of several examples in other methodologies.</p>
  <blockquote>
  <p>While I was looking for examples of Transfer Learning, most seemed to be based on image data, audio data, or text data. I was not able to find an example of training a neural network on numerical data.</p>
  </blockquote>
  <p>All the cases you say are converted to numerical data. Image and audio usually via sampling, text via one-hot encoding.</p>
  <blockquote>
  <p>I want to use transfer learning in this particular scenario: I have a lot of numerical data from an old environment, with binary classification labels, and utilizing this, want to train on a new environment to do the same binary classification.</p>
  </blockquote>
  <p>That is not transfer learning. Transfer learning applies when there are a change in the domain (input features) or in the task (output labels).</p>
  <blockquote>
  <p>The dataset would look something like this Sample Table
  Is this possible? What would the model look like?</p>
  </blockquote>
  <p>For a simple case as the one you present, probably a simple network with one hidden layer will be enough. Train it with original pairs of {features,label} or, if not available, use the current predictor to obtain the label from the features.</p>
  
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Answers.Text: Hi could you take a look here? https://ai.stackexchange.com/questions/27687/visualisation-for-mobilenet-feature-extraction
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Body: <p>It seems like transfer learning is only applicable to neural networks. Is this a correct assumption?</p>
  <p>While I was looking for examples of Transfer Learning, most seemed to be based on image data, audio data, or text data. I was not able to find an example of training a neural network on numerical data.</p>
  <p>I want to use transfer learning in this particular scenario: I have a lot of numerical data from an old environment, with binary classification labels, and utilizing this, want to train on a new environment to do the same binary classification.</p>
  <p>The dataset would look something like this <a href="https://i.sstatic.net/1CKRY.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/1CKRY.png" alt="Sample Table" /></a></p>
  <p>Is this possible? What would the model look like?</p>
  
Tags: <transfer-learning>
Title: Transfer Learning of Numerical Data
UserDisplayName: Sung Kyu Park
ThreadText: # Transfer Learning of Numerical Data
  
  (Asked by: username_0 on 2020-10-18)
  
  It seems like transfer learning is only applicable to neural networks. Is this a correct assumption?
  
  While I was looking for examples of Transfer Learning, most seemed to be based on image data, audio data, or text data. I was not able to find an example of training a neural network on numerical data.
  
  I want to use transfer learning in this particular scenario: I have a lot of numerical data from an old environment, with binary classification labels, and utilizing this, want to train on a new environment to do the same binary classification.
  
  The dataset would look something like this [![Sample Table](https://i.sstatic.net/1CKRY.png)](https://i.sstatic.net/1CKRY.png)
  
  Is this possible? What would the model look like?
  
  **Tags:** transfer-learning
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2020-10-18. Score: 1 | Accepted answer
  
  > It seems like transfer learning is only applicable to neural networks. Is this a correct assumption?
  
  No. [Wiki](https://en.wikipedia.org/wiki/Transfer_learning) page give you pointers of several examples in other methodologies.
  
  > While I was looking for examples of Transfer Learning, most seemed to be based on image data, audio data, or text data. I was not able to find an example of training a neural network on numerical data.
  
  All the cases you say are converted to numerical data. Image and audio usually via sampling, text via one-hot encoding.
  
  > I want to use transfer learning in this particular scenario: I have a lot of numerical data from an old environment, with binary classification labels, and utilizing this, want to train on a new environment to do the same binary classification.
  
  That is not transfer learning. Transfer learning applies when there are a change in the domain (input features) or in the task (output labels).
  
  > The dataset would look something like this Sample Table
  > Is this possible? What would the model look like?
  
  For a simple case as the one you present, probably a simple network with one hidden layer will be enough. Train it with original pairs of {features,label} or, if not available, use the current predictor to obtain the label from the features.
  
  > **Comments:**
  > **username_2**: Hi could you take a look here? https://ai.stackexchange.com/questions/27687/visualisation-for-mobilenet-feature-extraction  
  
  ---
  
  