<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>

CC BY-SA 4.0
2021-05-06T23:11:49.437Z
Hi could you take a look here? https://ai.stackexchange.com/questions/27687/visualisation-for-mobilenet-feature-extraction
x89
CC BY-SA 4.0
2020-10-18T19:03:02.283Z
2020-10-18T19:09:59.280Z
2020-10-18T19:09:59.280Z
pasaba por aqui
<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>

<transfer-learning>
Transfer Learning of Numerical Data
Sung Kyu Park
# 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


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## 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  

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