From 1057547cbf85240d91d185bc73cdfcd8f453bebd Mon Sep 17 00:00:00 2001
From: pritesh2000 <priteshptadvi29@gmail.com>
Date: Tue, 3 Sep 2024 19:41:10 +0530
Subject: [PATCH] all typos done

---
 03_pytorch_computer_vision.ipynb | 18 ++++++------------
 1 file changed, 6 insertions(+), 12 deletions(-)

diff --git a/03_pytorch_computer_vision.ipynb b/03_pytorch_computer_vision.ipynb
index 9ed1fff6..9269afc0 100644
--- a/03_pytorch_computer_vision.ipynb
+++ b/03_pytorch_computer_vision.ipynb
@@ -3686,7 +3686,7 @@
     ">\n",
     "> Generally, the more CPU cores you have, the faster your models will train on CPU. And similar for GPUs.\n",
     "> \n",
-    "> Newer hardware (in terms of age) will also often train models faster due to incorporating technology advances.\n",
+    "> Newer hardware (in terms of age) will also often train models faster due to incorporating technological advances.\n",
     "\n",
     "How about we get visual?"
    ]
@@ -4041,7 +4041,7 @@
     "\n",
     "One of the most visual is a [confusion matrix](https://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/).\n",
     "\n",
-    "A confusion matrix shows you where your classification model got confused between predicitons and true labels.\n",
+    "A confusion matrix shows you where your classification model got confused between predictions and true labels.\n",
     "\n",
     "To make a confusion matrix, we'll go through three steps:\n",
     "1. Make predictions with our trained model, `model_2` (a confusion matrix compares predictions to true labels).\n",
@@ -4126,7 +4126,7 @@
     "2. Make a confusion matrix using [`torchmetrics.ConfusionMatrix`](https://torchmetrics.readthedocs.io/en/latest/references/modules.html?highlight=confusion#confusionmatrix).\n",
     "3. Plot the confusion matrix using [`mlxtend.plotting.plot_confusion_matrix()`](http://rasbt.github.io/mlxtend/user_guide/plotting/plot_confusion_matrix/).\n",
     "\n",
-    "First we'll need to make sure we've got `torchmetrics` and `mlxtend` installed (these two libraries will help us make and visual a confusion matrix).\n",
+    "First we'll need to make sure we've got `torchmetrics` and `mlxtend` installed (these two libraries will help us make and visualize a confusion matrix).\n",
     "\n",
     "> **Note:** If you're using Google Colab, the default version of `mlxtend` installed is 0.14.0 (as of March 2022), however, for the parameters of the `plot_confusion_matrix()` function we'd like use, we need 0.19.0 or higher. "
    ]
@@ -4215,7 +4215,7 @@
     "\n",
     "Then we'll create a confusion matrix (in tensor format) by passing our instance our model's predictions (`preds=y_pred_tensor`) and targets (`target=test_data.targets`).\n",
     "\n",
-    "Finally we can plot our confision matrix using the `plot_confusion_matrix()` function from `mlxtend.plotting`."
+    "Finally we can plot our confusion matrix using the `plot_confusion_matrix()` function from `mlxtend.plotting`."
    ]
   },
   {
@@ -4539,7 +4539,7 @@
     "7. Turn the MNIST train and test datasets into dataloaders using `torch.utils.data.DataLoader`, set the `batch_size=32`.\n",
     "8. Recreate `model_2` used in this notebook (the same model from the [CNN Explainer website](https://poloclub.github.io/cnn-explainer/), also known as TinyVGG) capable of fitting on the MNIST dataset.\n",
     "9. Train the model you built in exercise 8. on CPU and GPU and see how long it takes on each.\n",
-    "10. Make predictions using your trained model and visualize at least 5 of them comparing the prediciton to the target label.\n",
+    "10. Make predictions using your trained model and visualize at least 5 of them comparing the prediction to the target label.\n",
     "11. Plot a confusion matrix comparing your model's predictions to the truth labels.\n",
     "12. Create a random tensor of shape `[1, 3, 64, 64]` and pass it through a `nn.Conv2d()` layer with various hyperparameter settings (these can be any settings you choose), what do you notice if the `kernel_size` parameter goes up and down?\n",
     "13. Use a model similar to the trained `model_2` from this notebook to make predictions on the test [`torchvision.datasets.FashionMNIST`](https://pytorch.org/vision/main/generated/torchvision.datasets.FashionMNIST.html) dataset. \n",
@@ -4549,16 +4549,10 @@
     "\n",
     "## Extra-curriculum\n",
     "* **Watch:** [MIT's Introduction to Deep Computer Vision](https://www.youtube.com/watch?v=iaSUYvmCekI&list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI&index=3) lecture. This will give you a great intuition behind convolutional neural networks.\n",
-    "* Spend 10-minutes clicking thorugh the different options of the [PyTorch vision library](https://pytorch.org/vision/stable/index.html), what different modules are available?\n",
+    "* Spend 10-minutes clicking through the different options of the [PyTorch vision library](https://pytorch.org/vision/stable/index.html), what different modules are available?\n",
     "* Lookup \"most common convolutional neural networks\", what architectures do you find? Are any of them contained within the [`torchvision.models`](https://pytorch.org/vision/stable/models.html) library? What do you think you could do with these?\n",
     "* For a large number of pretrained PyTorch computer vision models as well as many different extensions to PyTorch's computer vision functionalities check out the [PyTorch Image Models library `timm`](https://github.com/rwightman/pytorch-image-models/) (Torch Image Models) by Ross Wightman."
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3690b822",
-   "metadata": {},
-   "source": []
   }
  ],
  "metadata": {