Merge pull request #17057 from deroneriksson/low_level_intro_typos
Fix typos in low-level introduction documentation
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@ -295,7 +295,7 @@ the same input. @{tf.layers$Layers} are the preferred way to add trainable
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parameters to a graph.
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Layers package together both the variables and the operations that act
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on them, . For example a
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on them. For example a
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[densely-connected layer](https://developers.google.com/machine-learning/glossary/#fully_connected_layer)
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performs a weighted sum across all inputs
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for each output and applies an optional
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@ -478,7 +478,7 @@ good. Here's what we got; your own output will almost certainly differ:
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[ 0.10527515]]
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```
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### loss
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### Loss
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To optimize a model, you first need to define the loss. We'll use the mean
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square error, a standard loss for regression problems.
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