more boring typos
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@@ -380,8 +380,8 @@ extension of it into a deep neural network would (hopefully) be
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painless.
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<p>
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For simplicity, we assume that the input is an array \( \vec x =
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(x_1, \dots, x_N) \) with \( N \) elements. It is at these points the neural
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For simplicity, we assume that the input is an array
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\( \hat{x}= (x_1, \dots, x_N) \) with \( N \) elements. It is at these points the neural
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network should find \( P \) such that it fulfills <a href="._NeuralNet-bs092.html#mjx-eqn-19">(19)</a>.
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<p>
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