some typos

04_pytorch_custom_datasets.ipynb

@@ -166,7 +166,7 @@
    "source": [
     "## 1. Get data\n",
     "\n",
-    "First thing's first we need some data.\n",
+    "First things first we need some data.\n",
     "\n",
     "And like any good cooking show, some data has already been prepared for us.\n",
     "\n",
@@ -270,7 +270,7 @@
     "\n",
     "In our case, we have images of pizza, steak and sushi in standard image classification format.\n",
     "\n",
-    "Image classification format contains separate classes of images in seperate directories titled with a particular class name.\n",
+    "Image classification format contains separate classes of images in separate directories titled with a particular class name.\n",
     "\n",
     "For example, all images of `pizza` are contained in the `pizza/` directory.\n",
     "\n",
@@ -973,7 +973,7 @@
     "\n",
     "We'll do so using [`torch.utils.data.DataLoader`](https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader).\n",
     "\n",
-    "Turning our `Dataset`'s into `DataLoader`'s makes them iterable so a model can go through learn the relationships between samples and targets (features and labels).\n",
+    "Turning our `Dataset`'s into `DataLoader`'s makes them iterable so a model can go through and learn the relationships between samples and targets (features and labels).\n",
     "\n",
     "To keep things simple, we'll use a `batch_size=1` and `num_workers=1`.\n",
     "\n",
@@ -1759,7 +1759,7 @@
    "source": [
     "They sure do!\n",
     "\n",
-    "Let's now take a lot at some other forms of data transforms."
+    "Let's now take a look at some other forms of data transforms."
    ]
   },
   {
@@ -1778,7 +1778,7 @@
     "\n",
     "Or cropping it or randomly erasing a portion or randomly rotating them.\n",
     "\n",
-    "Doing this kinds of transforms is often referred to as **data augmentation**.\n",
+    "Doing these kinds of transforms is often referred to as **data augmentation**.\n",
     "\n",
     "**Data augmentation** is the process of altering your data in such a way that you *artificially* increase the diversity of your training set.\n",
     "\n",