adj, verb, typos

Author: pritesh2000

07_pytorch_experiment_tracking.ipynb

@@ -276,7 +276,7 @@
     "\n",
     "Let's create a function to \"set the seeds\" called `set_seeds()`.\n",
     "\n",
-    "> **Note:** Recall a [random seed](https://en.wikipedia.org/wiki/Random_seed) is a way of flavouring the randomness generated by a computer. They aren't necessary to always set when running machine learning code, however, they help ensure there's an element of reproducibility (the numbers I get with my code are similar to the numbers you get with your code). Outside of an education or experimental setting, random seeds generally aren't required."
+    "> **Note:** Recalling a [random seed](https://en.wikipedia.org/wiki/Random_seed) is a way of flavouring the randomness generated by a computer. They aren't necessary to always set when running machine learning code, however, they help ensure there's an element of reproducibility (the numbers I get with my code are similar to the numbers you get with your code). Outside of an educational or experimental setting, random seeds generally aren't required."
    ]
   },
   {
@@ -313,7 +313,7 @@
     "\n",
     "So how about we run some experiments and try to further improve our results?\n",
     "\n",
-    "To do so, we'll use similar code to the previous section to download the [`pizza_steak_sushi.zip`](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/data/pizza_steak_sushi.zip) (if the data doesn't already exist) except this time its been functionised.\n",
+    "To do so, we'll use similar code to the previous section to download the [`pizza_steak_sushi.zip`](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/data/pizza_steak_sushi.zip) (if the data doesn't already exist) except this time it's been functionalised.\n",
     "\n",
     "This will allow us to use it again later. "
    ]
@@ -421,7 +421,7 @@
     "\n",
     "And since we'll be using transfer learning and specifically pretrained models from [`torchvision.models`](https://pytorch.org/vision/stable/models.html), we'll create a transform to prepare our images correctly.\n",
     "\n",
-    "To transform our images in tensors, we can use:\n",
+    "To transform our images into tensors, we can use:\n",
     "1. Manually created transforms using `torchvision.transforms`.\n",
     "2. Automatically created transforms using `torchvision.models.MODEL_NAME.MODEL_WEIGHTS.DEFAULT.transforms()`.\n",
     "    * Where `MODEL_NAME` is a specific `torchvision.models` architecture, `MODEL_WEIGHTS` is a specific set of pretrained weights and `DEFAULT` means the \"best available weights\".\n",
@@ -959,7 +959,7 @@
    "source": [
     "> **Note:** You might notice the results here are slightly different to what our model got in 06. PyTorch Transfer Learning. The difference comes from using the `engine.train()` and our modified `train()` function. Can you guess why? The [PyTorch documentation on randomness](https://pytorch.org/docs/stable/notes/randomness.html) may help more.\n",
     "\n",
-    "Running the cell above we get similar outputs we got in [06. PyTorch Transfer Learning section 4: Train model](https://www.learnpytorch.io/06_pytorch_transfer_learning/#4-train-model) but the difference is behind the scenes our `writer` instance has created a `runs/` directory storing our model's results.\n",
+    "Running the cell above we get similar outputs we got in [06. PyTorch Transfer Learning section 4: Train model](https://www.learnpytorch.io/06_pytorch_transfer_learning/#4-train-model) but the difference is that behind the scenes our `writer` instance has created a `runs/` directory storing our model's results.\n",
     "\n",
     "For example, the save location might look like:\n",
     "\n",