minor change

Author: PhilKang0704

learn-pr/student-evangelism/analyze-review-sentiment-with-keras/includes/2-build-and-train-a-neural-network.md

@@ -116,7 +116,7 @@ In this unit, you'll use Keras to build and train a neural network that analyzes
 
     The call to the [compile](https://keras.io/models/model/#compile) function "compiles" the model by specifying important parameters such as which [optimizer](https://keras.io/optimizers/) to use and what [metrics](https://keras.io/metrics/) to use to judge the accuracy of the model in each training step. Training doesn't begin until you call the model's `fit` function, so the `compile` call typically executes quickly.
 
-2. Now call the [fit](https://keras.io/models/model/#fit) function to train the neural network:
+1. Now call the [fit](https://keras.io/models/model/#fit) function to train the neural network:
 
     ```python
     hist = model.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=5, batch_size=128)
@@ -130,7 +130,7 @@ In this unit, you'll use Keras to build and train a neural network that analyzes
 
     _Training the model_
 
-3. This model is unusual in that it learns well with just a few epochs. The training accuracy quickly zooms to near 100%, while the validation accuracy goes up for an epoch or two and then flattens out. You generally don't want to train a model for any longer than is required for these accuracies to stabilize. The risk is [overfitting](https://en.wikipedia.org/wiki/Overfitting), which results in the model performing well against test data but not so well with real-world data. One indication that a model is overfitting is a growing discrepancy between the training accuracy and the validation accuracy. For a great introduction to overfitting, see [Overfitting in Machine Learning: What It Is and How to Prevent It](https://elitedatascience.com/overfitting-in-machine-learning).
+1. This model is unusual in that it learns well with just a few epochs. The training accuracy quickly zooms to near 100%, while the validation accuracy goes up for an epoch or two and then flattens out. You generally don't want to train a model for any longer than is required for these accuracies to stabilize. The risk is [overfitting](https://en.wikipedia.org/wiki/Overfitting), which results in the model performing well against test data but not so well with real-world data. One indication that a model is overfitting is a growing discrepancy between the training accuracy and the validation accuracy. For a great introduction to overfitting, see [Overfitting in Machine Learning: What It Is and How to Prevent It](https://elitedatascience.com/overfitting-in-machine-learning).
 
     To visualize the changes in training and validation accuracy as training progress, execute the following statements in a new notebook cell:
 
@@ -155,15 +155,15 @@ In this unit, you'll use Keras to build and train a neural network that analyzes
 
     The accuracy data comes from the `history` object returned by the model's `fit` function. Based on the chart that you see, would you recommend increasing the number of training epochs, decreasing it, or leaving it the same?
 
-4. Another way to check for overfitting is to compare training loss to validation loss as training proceeds. Optimization problems such as this seek to minimize a loss function. You can read more [here](https://en.wikipedia.org/wiki/Loss_function). For a given epoch, training loss, much greater than validation loss, can be evidence of overfitting. In the previous step, you used the `acc` and `val_acc` properties of the `history` object's `history` property to plot training and validation accuracy. The same property also contains values named `loss` and `val_loss` representing training and validation loss, respectively. If you wanted to plot these values to produce a chart like the one below, how would you modify the code above to do it?
+1. Another way to check for overfitting is to compare training loss to validation loss as training proceeds. Optimization problems such as this seek to minimize a loss function. You can read more [here](https://en.wikipedia.org/wiki/Loss_function). For a given epoch, training loss, much greater than validation loss, can be evidence of overfitting. In the previous step, you used the `acc` and `val_acc` properties of the `history` object's `history` property to plot training and validation accuracy. The same property also contains values named `loss` and `val_loss` representing training and validation loss, respectively. If you wanted to plot these values to produce a chart like the one below, how would you modify the code above to do it?
 
     ![Training and validation loss.](../media/2-loss-chart.png)
 
     _Training and validation loss_
 
     Given that the gap between training and validation loss begins increasing in the third epoch, what would you say if someone suggested that you increase the number of epochs to 10 or 20?
 
-5. Finish up by calling the model's `evaluate` method to determine how accurately the model is able to quantify the sentiment expressed in text based on the test data in `x_test` (reviews) and `y_test` (0s and 1s, or "labels," indicating which reviews are positive and which are negative):
+1. Finish up by calling the model's `evaluate` method to determine how accurately the model is able to quantify the sentiment expressed in text based on the test data in `x_test` (reviews) and `y_test` (0s and 1s, or "labels," indicating which reviews are positive and which are negative):
 
     ```python
     scores = model.evaluate(x_test, y_test, verbose=0)