update Lux Linear Regression

notebooks/chapter_linear_regression/Concise Implementation of Linear Regression.ipynb

@@ -147,7 +147,7 @@
     "\n",
     "For standard operations, we can use a framework’s predefined layers, which allow us to focus on the layers used to construct the model rather than worrying about their implementation. Recall the architecture of a single-layer network as described in Fig. 3.1.2. The layer is called fully connected, since each of its inputs is connected to each of its outputs by means of a matrix-vector multiplication.\n",
     "\n",
-    "In Flux, a `Dense(2 => 1)` layer denotes a layer of one neuron with two inputs (two feature) and one output. "
+    "In Lux, a `Dense(2 => 1)` layer denotes a layer of one neuron with two inputs (two feature) and one output. "
    ]
   },
   {
@@ -187,7 +187,7 @@
    "id": "c717e1ec-be46-4e88-b238-5a3b3dbc745a",
    "metadata": {},
    "source": [
-    "The `Flux.mse` function computes the mean squared error. By default, `Flux.mse` returns the average loss over examples. It is faster (and easier to use) than implementing our own."
+    "The `MSELoss()` function computes the mean squared error. By default, `MSELoss()` returns the average loss over examples. It is faster (and easier to use) than implementing our own."
    ]
   },
   {