update gitignore and title

raymond-yuan · raymond-yuan · commit 4e0445c423ea · 2018-08-01T16:14:36.000-07:00
diff --git a/.gitignore b/.gitignore
@@ -90,3 +90,5 @@ ENV/
 
 # PyCharm
 .idea/
+
+samples/outreach/blogs/segmentation_blogpost/carvana-image-masking-challenge/
diff --git a/samples/outreach/blogs/segmentation_blogpost/Image Segmentation Blog Post.ipynb b/samples/outreach/blogs/segmentation_blogpost/Image Segmentation Blog Post.ipynb
@@ -51,7 +51,9 @@
     "**Audience:** This post is geared towards intermediate users who are comfortable with basic machine learning concepts.\n",
     "Note that if you wish to run this notebook, it is highly recommended that you do so with a GPU. \n",
     "\n",
-    "**Time Estimated**: 60 min\n"
+    "**Time Estimated**: 60 min\n",
+    "\n",
+    "By: Raymond Yuan, Software Engineering Intern"
    ]
   },
   {
@@ -156,6 +158,15 @@
     "Caution, large download ahead - downloading all files will require 14GB of diskspace. "
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "competition_name = 'carvana-image-masking-challenge'"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -174,7 +185,6 @@
     "    load_data_from_zip(competition, 'train_masks.zip')\n",
     "    load_data_from_zip(competition, 'train_masks.csv.zip')\n",
     "    \n",
-    "competition_name = 'carvana-image-masking-challenge'\n",
     "get_data(competition_name)"
    ]
   },
@@ -1490,22 +1500,6 @@
     "* **Custom Losses and Metrics** - We implemented custom metrics that allow us to see exactly what we need during training time. In addition, we wrote a custom loss function that is specifically suited to our task.  \n",
     "* **Save and load our model** - We saved our best model that we encountered according to our specified metric. When we wanted to perform inference with out best model, we loaded it from disk. Note that saving the model capture more than just the weights of the model: by default, it saves the model architecture, weights, as well as information about the training process such as the state of the optimizer, etc. "
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "autoexec": {
-      "startup": false,
-      "wait_interval": 0
-     }
-    },
-    "colab_type": "code",
-    "id": "ZnewDc0uc21r"
-   },
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
