Applied Deep Learning with TensorFlow 2: Learn to Implement Advanced Deep Learning Techniques with Python

© Umberto Michelucci 2022

U. MichelucciApplied Deep Learning with TensorFlow 2https://doi.org/10.1007/978-1-4842-8020-1_4

4. Regularization

Umberto Michelucci¹  

(1)

Dübendorf, Switzerland

 

This chapter explains a very important technique often used when training deep networks: regularization. We look at techniques such as the ℓ₁ and ℓ₂ methods, dropout, and early stopping. You learn how these methods help prevent the problem of overfitting and help you achieve much better results from your models when applied correctly. We look at the mathematics behind the methods and at how to implement them correctly in Python and Keras.

Complex Networks and Overfitting

In the previous chapters, you learned how to build and train complex networks. One of the most ...