Typo

05a - Deep Neural Networks (PyTorch).ipynb

@@ -395,7 +395,7 @@
  "source": [
  "## Evaluate model performance\n",
  "\n",
- "So, is the model any good? The raw accuracy reported from the validation data would seem to indicate that it predicts pretty well; but it's typically useful to dig a little deeper and compare the predictions for each possible class. A common way to visualize the performace of a classification model is to create a *confusion matrix* that shows a crosstab of correct and incorrect predictions for each class."
+ "So, is the model any good? The raw accuracy reported from the validation data would seem to indicate that it predicts pretty well; but it's typically useful to dig a little deeper and compare the predictions for each possible class. A common way to visualize the performance of a classification model is to create a *confusion matrix* that shows a crosstab of correct and incorrect predictions for each class."
  ]
  },
  {

05a - Deep Neural Networks (TensorFlow).ipynb

@@ -319,7 +319,7 @@
  "source": [
  "## Evaluate model performance\n",
  "\n",
- "So, is the model any good? The raw accuracy reported from the validation data would seem to indicate that it predicts pretty well; but it's typically useful to dig a little deeper and compare the predictions for each possible class. A common way to visualize the performace of a classification model is to create a *confusion matrix* that shows a crosstab of correct and incorrect predictions for each class."
+ "So, is the model any good? The raw accuracy reported from the validation data would seem to indicate that it predicts pretty well; but it's typically useful to dig a little deeper and compare the predictions for each possible class. A common way to visualize the performance of a classification model is to create a *confusion matrix* that shows a crosstab of correct and incorrect predictions for each class."
  ]
  },
  {