Answers.Body: <p>A loss function is what helps you &quot;train&quot; your neural network to do what you want it to do. A better way to word it to begin with would be an &quot;objective&quot; function. This function describes what objective you'd like your neural network to fit to (or to be good at).</p>
  <p>The loss function that you've described is &quot;squared error&quot;, which, as the name suggests, is the squared difference between the expected output and the output from the neural network. This trains the network to match the expected output value.</p>
  <p>Other loss (or &quot;objective&quot;) functions could train your network to look for different things. For example, training on cross entropy loss helps your network learn certain probabilities. That's why it's usually used for classification, like when you want to determine which digit from 0-9 was fed into your MNIST classifier.</p>
  
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Answers.CreationDate: 2020-11-03T14:26:17.320Z
Answers.LastActivityDate: 2020-11-03T14:26:17.320Z
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Body: <p>I have read what the loss function is, but I am not sure if I have understood it. For each neuron in the output layer, the loss function is usually equal to the square of the difference value of the neuron and the result we want. Is that correct?</p>
  
Tags: <neural-networks><objective-functions><mean-squared-error>
Title: What is the definition of a loss function in the context of neural networks?
UserDisplayName: Se1fie
ThreadText: # What is the definition of a loss function in the context of neural networks?
  
  (Asked by: username_0 on 2020-11-03)
  
  I have read what the loss function is, but I am not sure if I have understood it. For each neuron in the output layer, the loss function is usually equal to the square of the difference value of the neuron and the result we want. Is that correct?
  
  **Tags:** neural-networks, objective-functions, mean-squared-error
  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2020-11-03. Score: 1 | Accepted answer
  
  A loss function is what helps you "train" your neural network to do what you want it to do. A better way to word it to begin with would be an "objective" function. This function describes what objective you'd like your neural network to fit to (or to be good at).
  
  The loss function that you've described is "squared error", which, as the name suggests, is the squared difference between the expected output and the output from the neural network. This trains the network to match the expected output value.
  
  Other loss (or "objective") functions could train your network to look for different things. For example, training on cross entropy loss helps your network learn certain probabilities. That's why it's usually used for classification, like when you want to determine which digit from 0-9 was fed into your MNIST classifier.
  
  
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