Removed timing information

introduction_to_amazon_algorithms/imageclassification_caltech/Image-classification-fulltraining.ipynb

@@ -114,7 +114,7 @@
     "\n",
     "In this demo, we are using [Caltech-256](http://www.vision.caltech.edu/Image_Datasets/Caltech256/) dataset, which contains 30608 images of 256 objects. For the training and validation data, we follow the splitting scheme in this MXNet [example](https://github.com/apache/incubator-mxnet/blob/master/example/image-classification/data/caltech256.sh). In particular, it randomly selects 60 images per class for training, and uses the remaining data for validation. The algorithm takes `RecordIO` file as input. The user can also provide the image files as input, which will be converted into `RecordIO` format using MXNet's [im2rec](https://mxnet.incubator.apache.org/how_to/recordio.html?highlight=im2rec) tool. It takes around 50 seconds to converted the entire Caltech-256 dataset (~1.2GB) on a p2.xlarge instance. However, for this demo, we will use record io format. \n",
     "\n",
-    "Once we have the data available in the correct format for training, the next step is to actually train the model using the data. After setting training parameters, we kick off training, and poll for status until training is completed, which in this example, takes around 17 minutes (17 mins -- single machine with RecordIO, 15 mins -- 2 machines with RecordIO, 18 mins-- single machine with jpg, 18 mins -- 2 machines with jpg.).\n",
+    "Once we have the data available in the correct format for training, the next step is to actually train the model using the data. After setting training parameters, we kick off training, and poll for status until training is completed.\n",
     "\n",
     "## Training parameters\n",
     "There are two kinds of parameters that need to be set for training. The first one are the parameters for the training job. These include:\n",