all typos done

08_pytorch_paper_replicating.ipynb

@@ -4103,7 +4103,7 @@
         "id": "45a65cda-db08-441c-9f60-cf79138e029d"
       },
       "source": [
-        "Then we'll setup device-agonistc code."
+        "Then we'll setup device-agnostic code."
       ]
     },
     {
@@ -4327,7 +4327,7 @@
       "source": [
         "Finally, we'll transform our images into tensors and turn the tensors into DataLoaders.\n",
         "\n",
-        "Since we're using a pretrained model form `torchvision.models` we can call the `transforms()` method on it to get its required transforms.\n",
+        "Since we're using a pretrained model from `torchvision.models` we can call the `transforms()` method on it to get its required transforms.\n",
         "\n",
         "Remember, if you're going to use a pretrained model, it's generally important to **ensure your own custom data is transformed/formatted in the same way the data the original model was trained on**.\n",
         "\n",
@@ -4372,7 +4372,7 @@
       "source": [
         "And now we've got transforms ready, we can turn our images into DataLoaders using the `data_setup.create_dataloaders()` method we created in [05. PyTorch Going Modular section 2](https://www.learnpytorch.io/05_pytorch_going_modular/#2-create-datasets-and-dataloaders-data_setuppy).\n",
         "\n",
-        "Since we're using a feature extractor model (less trainable parameters), we could increase the batch size to a higher value (if we set it to 1024, we'd be mimicing an improvement found in [*Better plain ViT baselines for ImageNet-1k*](https://arxiv.org/abs/2205.01580), a paper which improves upon the original ViT paper and suggested extra reading). But since we only have ~200 training samples total, we'll stick with 32."
+        "Since we're using a feature extractor model (less trainable parameters), we could increase the batch size to a higher value (if we set it to 1024, we'd be mimicking an improvement found in [*Better plain ViT baselines for ImageNet-1k*](https://arxiv.org/abs/2205.01580), a paper which improves upon the original ViT paper and suggested extra reading). But since we only have ~200 training samples total, we'll stick with 32."
       ]
     },
     {
@@ -4649,7 +4649,7 @@
         "\n",
         "> **Note:** ^ the EffNetB2 model in reference was trained with 20% of pizza, steak and sushi data (double the amount of images) rather than the ViT feature extractor which was trained with 10% of pizza, steak and sushi data. An exercise would be to train the ViT feature extractor model on the same amount of data and see how much the results improve.\n",
         "\n",
-        "The EffNetB2 model is ~11x smaller than the ViT model with similiar results for test loss and accuracy.\n",
+        "The EffNetB2 model is ~11x smaller than the ViT model with similar results for test loss and accuracy.\n",
         "\n",
         "However, the ViT model's results may improve more when trained with the same data (20% pizza, steak and sushi data).\n",
         "\n",