Body: <p>Just an idea I am sure I read in a book some time ago, but I can't remember the name.</p>
  <p>Given a very large dataset and a neural network (or anything that can learn via something like stochastic gradient descent, passing a subset of samples to modify the model, as opposed to learning from the whole dataset at once), one can train a model for, say, classification.</p>
  <p>The idea was a methodology for selecting the samples that would make the model learn the most from, so you can spare the network from learning from examples that would make the model make only small changes, reducing computing time.</p>
  <p>I guess an easy methodology would pick at first a sample that is similar to a previous one but with another label, and pick the most similar on features and label samples at last. Does that make sense?</p>
  <p>Is there a googleable keyword for what I am talking about?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-07-27T07:41:53.553Z
Comments.Text: The keyword you may be looking for is active learning.
Comments.UserDisplayName: Floris den Hengst
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-08-06T10:29:47.760Z
Comments.Text: Why did you delete this post? The answer to it could be useful.
Comments.UserDisplayName: nbro
Tags: <neural-networks><stochastic-gradient-descent><batch-learning>
Title: Methodologies for passing the best samples for a neural network to learn
UserDisplayName: user4052054
ThreadText: # Methodologies for passing the best samples for a neural network to learn
  
  (Asked by: username_0 on 2021-07-23)
  
  Just an idea I am sure I read in a book some time ago, but I can't remember the name.
  
  Given a very large dataset and a neural network (or anything that can learn via something like stochastic gradient descent, passing a subset of samples to modify the model, as opposed to learning from the whole dataset at once), one can train a model for, say, classification.
  
  The idea was a methodology for selecting the samples that would make the model learn the most from, so you can spare the network from learning from examples that would make the model make only small changes, reducing computing time.
  
  I guess an easy methodology would pick at first a sample that is similar to a previous one but with another label, and pick the most similar on features and label samples at last. Does that make sense?
  
  Is there a googleable keyword for what I am talking about?
  
  **Tags:** neural-networks, stochastic-gradient-descent, batch-learning
  > **Comments:**
  > **username_1**: The keyword you may be looking for is active learning.  
  > **username_2**: Why did you delete this post? The answer to it could be useful.  
  
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  ## 0 answers
  
  