Answers.Body: <p>One application I know of being used in industry is of image classification, by only training the last layer of one of the inception models released by Google,  with the desired number of classes. I can't provide specific details. </p>
  
  <p>Transfer learning is useful when:</p>
  
  <ol>
  <li><p>You do not have the resources (time, processing power, etc.) to train a DL  model from scratch.</p></li>
  <li><p>You can compromise a bit on accuracy. </p></li>
  </ol>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-06-11T21:38:53.977Z
Answers.Text: It's also worth mentioning that transfer learning is useful when you have a small dataset and there's already a pre-trained neural network that has been trained to extract certain general features that could also be useful in the current task that you need to solve.
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Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-10-05T05:10:52.177Z
Answers.LastActivityDate: 2020-06-11T21:35:13.357Z
Answers.LastEditDate: 2020-06-11T21:35:13.357Z
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Body: <p>What are the real-life applications of transfer learning in machine learning? I am particularly interested in industrial applications of the concept.</p>
  
Tags: <machine-learning><applications><transfer-learning>
Title: What are the real-life applications of transfer learning?
UserDisplayName: Ali Usyd
ThreadText: # What are the real-life applications of transfer learning?
  
  (Asked by: username_0 on 2019-10-05)
  
  What are the real-life applications of transfer learning in machine learning? I am particularly interested in industrial applications of the concept.
  
  **Tags:** machine-learning, applications, transfer-learning
  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2019-10-05. Score: 7 | Accepted answer
  
  One application I know of being used in industry is of image classification, by only training the last layer of one of the inception models released by Google, with the desired number of classes. I can't provide specific details.
  
  Transfer learning is useful when:
  
  1. You do not have the resources (time, processing power, etc.) to train a DL model from scratch.
  2. You can compromise a bit on accuracy.
  
  > **Comments:**
  > **username_2**: It's also worth mentioning that transfer learning is useful when you have a small dataset and there's already a pre-trained neural network that has been trained to extract certain general features that could also be useful in the current task that you need to solve.  
  
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