"many batches" -> "mini-batches" (#402)

04_mnist_basics.ipynb

@@ -4180,7 +4180,7 @@
     "\n",
     "As we saw in our discussion of data augmentation in <<chapter_production>>, we get better generalization if we can vary things during training. One simple and effective thing we can vary is what data items we put in each mini-batch. Rather than simply enumerating our dataset in order for every epoch, instead what we normally do is randomly shuffle it on every epoch, before we create mini-batches. PyTorch and fastai provide a class that will do the shuffling and mini-batch collation for you, called `DataLoader`.\n",
     "\n",
-    "A `DataLoader` can take any Python collection and turn it into an iterator over many batches, like so:"
+    "A `DataLoader` can take any Python collection and turn it into an iterator over mini-batches, like so:"
    ]
   },
   {
@@ -4239,7 +4239,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "When we pass a `Dataset` to a `DataLoader` we will get back many batches which are themselves tuples of tensors representing batches of independent and dependent variables:"
+    "When we pass a `Dataset` to a `DataLoader` we will get back mini-batches which are themselves tuples of tensors representing batches of independent and dependent variables:"
    ]
   },
   {