Update 04_mnist_basics.ipynb

04_mnist_basics.ipynb

@@ -3937,7 +3937,7 @@
    "source": [
     "A very small change in the value of a weight will often not actually change the accuracy at all. This means it is not useful to use accuracy as a loss function—if we do, most of the time our gradients will actually be 0, and the model will not be able to learn from that number.\n",
     "\n",
-    "> S: In mathematical terms, accuracy is a function that is constant almost everywhere (except at the threshold, 0.5), so its derivative is nil almost everywhere (and infinity at the threshold). This then gives gradients that are 0 or infinite, which are useless for updating the model.\n",
+    "> S: In mathematical terms, accuracy is a function that is constant almost everywhere (except at the threshold, 0.5), so its derivative is null almost everywhere (and infinity at the threshold). This then gives gradients that are 0 or infinite, which are useless for updating the model.\n",
     "\n",
     "Instead, we need a loss function which, when our weights result in slightly better predictions, gives us a slightly better loss. So what does a \"slightly better prediction\" look like, exactly? Well, in this case, it means that if the correct answer is a 3 the score is a little higher, or if the correct answer is a 7 the score is a little lower.\n",
     "\n",