week41 exercises small changes

doc/LectureNotes/exercisesweek41.ipynb

@@ -327,7 +327,7 @@
    "id": "0da7fd52",
    "metadata": {},
    "source": [
-    "**d)** Why is a neural network with no activation functions always mathematically equivelent to a neural network with only one layer?\n"
+    "**d)** Why is a neural network with no activation functions mathematically equivelent to(can be reduced to) a neural network with only one layer?\n"
    ]
   },
   {
@@ -454,7 +454,7 @@
    "id": "a6349db6",
    "metadata": {},
    "source": [
-    "**b)** Make a matrix of inputs with the shape (number of features, number of inputs), you choose the number of inputs and features per input. Then complete the function `feed_forward_batch` so that you can process this matrix of inputs with only one matrix multiplication and one broadcasted vector addition per layer. (Hint: You will only need to swap two variable around from your previous implementation, but remember to test that you get the same results for equivelent inputs!)\n"
+    "**b)** Make a matrix of inputs with the shape (number of inputs, number of features), you choose the number of inputs and features per input. Then complete the function `feed_forward_batch` so that you can process this matrix of inputs with only one matrix multiplication and one broadcasted vector addition per layer. (Hint: You will only need to swap two variable around from your previous implementation, but remember to test that you get the same results for equivelent inputs!)"
    ]
   },
   {
@@ -480,7 +480,7 @@
    "id": "efd07b4e",
    "metadata": {},
    "source": [
-    "**c)** Create and evaluate a neural network with 4 inputs and layers with output sizes 12, 10, 3 and activations ReLU, ReLU, softmax.\n"
+    "**c)** Create and evaluate a neural network with 4 input features, and layers with output sizes 12, 10, 3 and activations ReLU, ReLU, softmax.\n"
    ]
   },
   {