© Umberto Michelucci 2018
Umberto MichelucciApplied Deep Learninghttps://doi.org/10.1007/978-1-4842-3790-8_5

5. Regularization

Umberto Michelucci1 
(1)
toelt.ai, Dübendorf, Switzerland
 

In this chapter, you will look at a very important technique often used when training deep networks: regularization. You will look at techniques such as the 2 and 1 methods, dropout, and early stopping. You will see how these methods help avoid the problem of overfitting and achieve much better results from your models, when applied correctly. You will look at the mathematics behind the methods and at how to implement it in Python and TensorFlow correctly.

Complex Networks and Overfitting

In the previous chapters, you have learned how to build and train complex networks. ...

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