Which workbook are you referring to?
Accuracy tells you how well the model is classifying the data points. Loss indicates how confident the model is when predicting the class of points.
Consider 1 batch of 3 points where all of them have the following predictions about the correct class:
>>> import numpy as np
>>> loss = lambda preds: -np.mean(np.log(preds))
>>> y_pred_weak_confidence = np.array([.51, .52, .56])
>>> loss(y_pred_weak_confidence)
0.6356965053077905
>>> y_pred_better_confidence = np.array([.99, .95, 1])
>>> loss(y_pred_better_confidence)
0.020447876747017344
>>>