Fixed typo

05b - Convolutional Neural Networks (PyTorch).ipynb

@@ -235,7 +235,7 @@
     "\n",
     "In the example below, we use 5 epochs to train the model using the batches of images loaded by the data loaders, holding back the data in the test data loader for validation. After each epoch, a loss function measures the error (*loss*) in the model and adjusts the weights (which were randomly generated for the first iteration) to try to improve accuracy. \n",
     "\n",
-    "> **Note**: We're only using 5 epochs to minimze the training time for this simple example. A real-world CNN is usually trained over more epochs than this. CNN model training is processor-intensive, involving a lot of matrix and vector-based operations; so it's recommended to perform this on a system that can leverage GPUs, which are optimized for these kinds of calculation. This will take a while to complete on a CPU-based system - status will be displayed as the training progresses."
+    "> **Note**: We're only using 5 epochs to minimize the training time for this simple example. A real-world CNN is usually trained over more epochs than this. CNN model training is processor-intensive, involving a lot of matrix and vector-based operations; so it's recommended to perform this on a system that can leverage GPUs, which are optimized for these kinds of calculation. This will take a while to complete on a CPU-based system - status will be displayed as the training progresses."
    ]
   },
   {

05b - Convolutional Neural Networks (Tensorflow).ipynb

@@ -193,7 +193,7 @@
     "\n",
     "With the layers of the CNN defined, we're ready to train the model using our image data. In the example below, we use 5 iterations (*epochs*) to train the model in 30-image batches, holding back 30% of the data for validation. After each epoch, the loss function measures the error (*loss*) in the model and adjusts the weights (which were randomly generated for the first iteration) to try to improve accuracy. \n",
     "\n",
-    "> **Note**: We're only using 5 epochs to minimze the training time for this simple example. A real-world CNN is usually trained over more epochs than this. CNN model training is processor-intensive, involving a lot of matrix and vector-based operations; so it's recommended to perform this on a system that can leverage GPUs, which are optimized for these kinds of calculation. This will take a while to complete on a CPU-based system - status will be displayed as the training progresses."
+    "> **Note**: We're only using 5 epochs to minimize the training time for this simple example. A real-world CNN is usually trained over more epochs than this. CNN model training is processor-intensive, involving a lot of matrix and vector-based operations; so it's recommended to perform this on a system that can leverage GPUs, which are optimized for these kinds of calculation. This will take a while to complete on a CPU-based system - status will be displayed as the training progresses."
    ]
   },
   {
@@ -430,4 +430,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}