From ac89ed370b710b997a4f29ff83ce471d6bb29fb5 Mon Sep 17 00:00:00 2001
From: k22036 <131113333+k22036@users.noreply.github.com>
Date: Sat, 4 Jan 2025 17:29:09 +0900
Subject: [PATCH] Fix typos in FashionMNIST notebook (#3321)

---
 examples/notebooks/FashionMNIST.ipynb | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/examples/notebooks/FashionMNIST.ipynb b/examples/notebooks/FashionMNIST.ipynb
index 62d4da67d74..d06470e507b 100644
--- a/examples/notebooks/FashionMNIST.ipynb
+++ b/examples/notebooks/FashionMNIST.ipynb
@@ -131,7 +131,7 @@
       "source": [
         "The code below first sets up transform using `torhvision transfroms` for converting images to pytorch tensors and normalizing the images.\n",
         "\n",
-        "Next, We use `torchvision datasets` for dowloading the fashion mnist dataset and applying transforms which we defined above.\n",
+        "Next, We use `torchvision datasets` for downloading the fashion mnist dataset and applying transforms which we defined above.\n",
         "\n",
         "* `trainset` contains the training data.\n",
         "* `validationset` contains the validation data\n",
@@ -342,7 +342,7 @@
       "source": [
         "### EarlyStopping - Tracking Validation Loss\n",
         "\n",
-        "Now we will setup a `EarlyStopping` handler for this training process. EarlyStopping requires a score_function that allows the user to define whatever criteria to stop trainig. In this case, if the loss of the validation set does not decrease in 10 epochs, the training process will stop early. Since the `EarlyStopping` handler relies on the validation loss, it's attached to the `val_evaluator`. "
+        "Now we will setup a `EarlyStopping` handler for this training process. EarlyStopping requires a score_function that allows the user to define whatever criteria to stop training. In this case, if the loss of the validation set does not decrease in 10 epochs, the training process will stop early. Since the `EarlyStopping` handler relies on the validation loss, it's attached to the `val_evaluator`. "
       ]
     },
     {
@@ -633,7 +633,7 @@
         "id": "cWXOEpQ4abyv"
       },
       "source": [
-        "### Refrences \n",
+        "### References  \n",
         "* [Pytorch Ignite Text CNN example notebook](https://github.com/pytorch/ignite/blob/master/examples/notebooks/TextCNN.ipynb)\n",
         "* [Pytorch Ignite MNIST example](https://github.com/pytorch/ignite/blob/master/examples/mnist/mnist.py)"
       ]