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-rw-r--r--tensorflow/examples/udacity/1_notmnist.ipynb6
1 files changed, 3 insertions, 3 deletions
diff --git a/tensorflow/examples/udacity/1_notmnist.ipynb b/tensorflow/examples/udacity/1_notmnist.ipynb
index 521cbf3000..39674e1aa4 100644
--- a/tensorflow/examples/udacity/1_notmnist.ipynb
+++ b/tensorflow/examples/udacity/1_notmnist.ipynb
@@ -70,7 +70,7 @@
"colab_type": "text"
},
"source": [
- "First, we'll download the dataset to our local machine. The data consists of characters rendered in a variety of fonts on a 28x28 image. The labels are limited to 'A' through 'J' (10 classes). The training set has about 500k and the testset 19000 labelled examples. Given these sizes, it should be possible to train models quickly on any machine."
+ "First, we'll download the dataset to our local machine. The data consists of characters rendered in a variety of fonts on a 28x28 image. The labels are limited to 'A' through 'J' (10 classes). The training set has about 500k and the testset 19000 labeled examples. Given these sizes, it should be possible to train models quickly on any machine."
]
},
{
@@ -109,7 +109,7 @@
"outputId": "0d0f85df-155f-4a89-8e7e-ee32df36ec8d"
},
"source": [
- "url = 'http://commondatastorage.googleapis.com/books1000/'\n",
+ "url = 'https://commondatastorage.googleapis.com/books1000/'\n",
"last_percent_reported = None\n",
"data_root = '.' # Change me to store data elsewhere\n",
"\n",
@@ -168,7 +168,7 @@
},
"source": [
"Extract the dataset from the compressed .tar.gz file.\n",
- "This should give you a set of directories, labelled A through J."
+ "This should give you a set of directories, labeled A through J."
]
},
{