Answers.Body: <p>There is really no difference between minimizing a cost function and maximizing a value function.  One can be the reciprocal of the other, or the negative of the other, for example.</p>
  
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Answers.CreationDate: 2019-10-14T06:56:35.773Z
Answers.Text: It is worth noting that the function that the OP gives is in fact the negative of the usual multi-class loss function. I would hazard a guess that Andrej used it to make a more direct comparison with RL.
Answers.UserDisplayName: Neil Slater
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Answers.CreationDate: 2019-10-14T04:22:04.797Z
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Body: <p>I was watching a video about policy gradients by Andrej Karpathy. At <a href="https://youtu.be/tqrcjHuNdmQ?t=604" rel="nofollow noreferrer">10:00</a>, it shows an equation for supervised learning for image classification.</p>
  <p><span class="math-container">$$\max\sum _{i} \log p(y_i \mid x_i)$$</span></p>
  <p>I have worked with image classification models before, but I always minimized a cost function (aka loss function). I have also never seen someone maximizing a cost function for image classification in the wild.</p>
  <ul>
  <li><p>So, what are the advantages of a minimizing loss function over a maximizing loss function in image classification?</p>
  </li>
  <li><p>Other than RL, which problems do we solve by maximizing a cost function?</p>
  </li>
  </ul>
  
Tags: <deep-learning><terminology><image-recognition><objective-functions><policy-gradients>
Title: In image classification, why do we usually minimize a cost function rather than maximizing it?
UserDisplayName: Eka
ThreadText: # In image classification, why do we usually minimize a cost function rather than maximizing it?
  
  (Asked by: username_0 on 2019-10-14)
  
  I was watching a video about policy gradients by Andrej Karpathy. At [10:00](https://youtu.be/tqrcjHuNdmQ?t=604), it shows an equation for supervised learning for image classification.
  
  $$\max\sum \_{i} \log p(y\_i \mid x\_i)$$
  
  I have worked with image classification models before, but I always minimized a cost function (aka loss function). I have also never seen someone maximizing a cost function for image classification in the wild.
  
  * So, what are the advantages of a minimizing loss function over a maximizing loss function in image classification?
  * Other than RL, which problems do we solve by maximizing a cost function?
  
  **Tags:** deep-learning, terminology, image-recognition, objective-functions, policy-gradients
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2019-10-14. Score: 1
  
  There is really no difference between minimizing a cost function and maximizing a value function. One can be the reciprocal of the other, or the negative of the other, for example.
  
  > **Comments:**
  > **username_2**: It is worth noting that the function that the OP gives is in fact the negative of the usual multi-class loss function. I would hazard a guess that Andrej used it to make a more direct comparison with RL.  
  
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