all typos done

09_pytorch_model_deployment.ipynb

@@ -3747,7 +3747,7 @@
     "\n",
     "We'll go from three classes to 101!\n",
     "\n",
-    "From pizza, steak, sushi to pizza, steak, sushi, hot dog, apple pie, carrot cake, chocolate cake, french fires, garlic bread, ramen, nachos, tacos and more!\n",
+    "From pizza, steak, sushi to pizza, steak, sushi, hot dog, apple pie, carrot cake, chocolate cake, french fries, garlic bread, ramen, nachos, tacos and more!\n",
     "\n",
     "How?\n",
     "\n",
@@ -3816,7 +3816,7 @@
     "                                                                                                                                                      \n",
     "Nice!\n",
     "\n",
-    "See how just like our EffNetB2 model for FoodVision Mini the base layers are frozen (these are pretrained on ImageNet) and the outer layers (the `classifier` layers) are trainble with an output shape of `[batch_size, 101]` (`101` for 101 classes in Food101). \n",
+    "See how just like our EffNetB2 model for FoodVision Mini the base layers are frozen (these are pretrained on ImageNet) and the outer layers (the `classifier` layers) are trainable with an output shape of `[batch_size, 101]` (`101` for 101 classes in Food101). \n",
     "\n",
     "Now since we're going to be dealing with a fair bit more data than usual, how about we add a little data augmentation to our transforms (`effnetb2_transforms`) to augment the training data.\n",
     "\n",
@@ -4371,7 +4371,7 @@
     "\n",
     "Our FoodVision Big model is capable of classifying 101 classes versus FoodVision Mini's 3 classes, a 33.6x increase!\n",
     "\n",
-    "How does this effect the model size?\n",
+    "How does this affect the model size?\n",
     "\n",
     "Let's find out."
    ]
@@ -4448,7 +4448,7 @@
     "* `app.py` contains our FoodVision Big Gradio app.\n",
     "* `class_names.txt` contains all of the class names for FoodVision Big.\n",
     "* `examples/` contains example images to use with our Gradio app.\n",
-    "* `model.py` contains the model defintion as well as any transforms associated with the model.\n",
+    "* `model.py` contains the model definition as well as any transforms associated with the model.\n",
     "* `requirements.txt` contains the dependencies to run our app such as `torch`, `torchvision` and `gradio`."
    ]
   },
@@ -4521,7 +4521,7 @@
    "source": [
     "### 11.2 Saving Food101 class names to file (`class_names.txt`)\n",
     "\n",
-    "Because there are so many classes in the Food101 dataset, instead of storing them as a list in our `app.py` file, let's saved them to a `.txt` file and read them in when necessary instead.\n",
+    "Because there are so many classes in the Food101 dataset, instead of storing them as a list in our `app.py` file, let's save them to a `.txt` file and read them in when necessary instead.\n",
     "\n",
     "We'll just remind ourselves what they look like first by checking out `food101_class_names`."
    ]
@@ -4708,7 +4708,7 @@
     "1. **Imports and class names setup** - The `class_names` variable will be a list for all of the Food101 classes rather than pizza, steak, sushi. We can access these via `demos/foodvision_big/class_names.txt`.\n",
     "2. **Model and transforms preparation** - The `model` will have `num_classes=101` rather than `num_classes=3`. We'll also be sure to load the weights from `\"09_pretrained_effnetb2_feature_extractor_food101_20_percent.pth\"` (our FoodVision Big model path).\n",
     "3. **Predict function** - This will stay the same as FoodVision Mini's `app.py`.\n",
-    "4. **Gradio app** - The Gradio interace will have different `title`, `description` and `article` parameters to reflect the details of FoodVision Big.\n",
+    "4. **Gradio app** - The Gradio interface will have different `title`, `description` and `article` parameters to reflect the details of FoodVision Big.\n",
     "\n",
     "We'll also make sure to save it to `demos/foodvision_big/app.py` using the `%%writefile` magic command."
    ]
@@ -4962,7 +4962,7 @@
     }
    ],
    "source": [
-    "# IPython is a library to help work with Python iteractively \n",
+    "# IPython is a library to help work with Python interactively\n",
     "from IPython.display import IFrame\n",
     "\n",
     "# Embed FoodVision Big Gradio demo as an iFrame\n",
@@ -5024,7 +5024,7 @@
     "    * What model architecture does it use?\n",
     "6. Write down 1-3 potential failure points of our deployed FoodVision models and what some potential solutions might be.\n",
     "    * For example, what happens if someone was to upload a photo that wasn't of food to our FoodVision Mini model?\n",
-    "7. Pick any dataset from [`torchvision.datasets`](https://pytorch.org/vision/stable/datasets.html) and train a feature extractor model on it using a model from [`torchvision.models`](https://pytorch.org/vision/stable/models.html) (you could use one of the model's we've already created, e.g. EffNetB2 or ViT) for 5 epochs and then deploy your model as a Gradio app to Hugging Face Spaces. \n",
+    "7. Pick any dataset from [`torchvision.datasets`](https://pytorch.org/vision/stable/datasets.html) and train a feature extractor model on it using a model from [`torchvision.models`](https://pytorch.org/vision/stable/models.html) (you could use one of the models we've already created, e.g. EffNetB2 or ViT) for 5 epochs and then deploy your model as a Gradio app to Hugging Face Spaces. \n",
     "    * You may want to pick smaller dataset/make a smaller split of it so training doesn't take too long.\n",
     "    * I'd love to see your deployed models! So be sure to share them in Discord or on the [course GitHub Discussions page](https://github.com/mrdbourke/pytorch-deep-learning/discussions)."
    ]
@@ -5043,7 +5043,7 @@
     "    * The [Gradio Blocks API](https://gradio.app/docs/#blocks) for more advanced workflows.\n",
     "    * The Hugging Face Course chapter on [how to use Gradio with Hugging Face](https://huggingface.co/course/chapter9/1).\n",
     "* Edge devices aren't limited to mobile phones, they include small computers like the Raspberry Pi and the PyTorch team have a [fantastic blog post tutorial](https://pytorch.org/tutorials/intermediate/realtime_rpi.html) on deploying a PyTorch model to one.\n",
-    "* For a fanstastic guide on developing AI and ML-powered applications, see [Google's People + AI Guidebook](https://pair.withgoogle.com/guidebook). One of my favourites is the section on [setting the right expectations](https://pair.withgoogle.com/guidebook/patterns#set-the-right-expectations).\n",
+    "* For a fantastic guide on developing AI and ML-powered applications, see [Google's People + AI Guidebook](https://pair.withgoogle.com/guidebook). One of my favourites is the section on [setting the right expectations](https://pair.withgoogle.com/guidebook/patterns#set-the-right-expectations).\n",
     "    * I covered more of these kinds of resources, including guides from Apple, Microsoft and more in the [April 2021 edition of Machine Learning Monthly](https://zerotomastery.io/blog/machine-learning-monthly-april-2021/) (a monthly newsletter I send out with the latest and greatest of the ML field).\n",
     "* If you'd like to speed up your model's runtime on CPU, you should be aware of [TorchScript](https://pytorch.org/tutorials/beginner/Intro_to_TorchScript_tutorial.html), [ONNX](https://pytorch.org/docs/stable/onnx.html) (Open Neural Network Exchange) and [OpenVINO](https://docs.openvino.ai/latest/notebooks/102-pytorch-onnx-to-openvino-with-output.html). Going from pure PyTorch to ONNX/OpenVINO models I've seen a ~2x+ increase in performance.\n",
     "* For turning models into a deployable and scalable API, see the [TorchServe library](https://pytorch.org/serve/).\n",