Add missing "to" (d2l-ai#151)

More details can be found here: https://ell.stackexchange.com/questions/16964/referred-to-as-or-referred-as

chapter_deep-learning-basics/linear-regression.md

@@ -95,7 +95,7 @@ While in deep learning, we can represent model structures visually using neural
 
 ![Linear regression is a single-layer neural network. ](../img/singleneuron.svg)
 
-In the neural network shown above, the inputs are $x_1, x_2, \ldots x_d$. Sometimes the number of inputs is also referred as feature dimension. In the above cases the number of inputs is $d$ and the number of outputs is $1$. It should be noted that we use the output directly as the output of linear regression.  Since the input layer does not involve any other nonlinearities or any further calculations, the number of layers is 1. Sometimes this setting is also referred to as a single neuron. Since all inputs are connected to all outputs (in this case it's just one), the layer is also referred to as a 'fully connected layer' or 'dense layer'.
+In the neural network shown above, the inputs are $x_1, x_2, \ldots x_d$. Sometimes the number of inputs is also referred to as feature dimension. In the above cases the number of inputs is $d$ and the number of outputs is $1$. It should be noted that we use the output directly as the output of linear regression.  Since the input layer does not involve any other nonlinearities or any further calculations, the number of layers is 1. Sometimes this setting is also referred to as a single neuron. Since all inputs are connected to all outputs (in this case it's just one), the layer is also referred to as a 'fully connected layer' or 'dense layer'.
 
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