Visualizing Decision Boundaries

Hi Aurelien,

Thanks a lot for your book, it helps me a lot to understand the basic concepts in a practical way.
Can you please help me to understand the below code in general visualizing Decision Boundaries.

```python
def plot_predictions(clf, axes):
x0s = np.linspace(axes[0], axes[1], 100)
x1s = np.linspace(axes[2], axes[3], 100)
x0, x1 = np.meshgrid(x0s, x1s)
X = np.c_[x0.ravel(), x1.ravel()]
y_pred = clf.predict(X).reshape(x0.shape)
y_decision = clf.decision_function(X).reshape(x0.shape)
plt.contourf(x0, x1, y_pred, cmap=plt.cm.brg, alpha=0.2)
plt.contourf(x0, x1, y_decision, cmap=plt.cm.brg, alpha=0.1)

plot_predictions(polynomial_svm_clf, [-1.5, 2.5, -1, 1.5])
plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])

save_fig("moons_polynomial_svc_plot")
plt.show()
```

I understand that we create features using `linspace` to use them for getting predictions.
But I am unable to understand how predictions in `contourf` visualizes decision boundary.

Also, regarding the part 2 of the book what steps should we follow to use with latest version of TensorFlow.

Thanks a lot in advance.

Regards
Deepak.