March 2020
Beginner to intermediate
342 pages
8h 38m
English
You learned three important concepts in the previous section:
Here is a general strategy to strike that balance: start with an overfitting model function that tracks tiny fluctuations in the data, and progressively make it smoother until you hit a good middle ground. That idea of smoothing out the model function is called “regularization,” and is the subject of this section.
In the previous chapter, we took the first step of the process I just described: we created a deep neural network that overfits the data at hand. Let’s take a closer look at that network’s model, and afterwards we’ll see how ...