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Mastering Machine Learning Algorithms

You're reading from   Mastering Machine Learning Algorithms Expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work

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Product type Paperback
Published in Jan 2020
Publisher Packt
ISBN-13 9781838820299
Length 798 pages
Edition 2nd Edition
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Authors (2):
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Giuseppe Bonaccorso Giuseppe Bonaccorso
Author Profile Icon Giuseppe Bonaccorso
Giuseppe Bonaccorso
Giuseppe Bonaccorso Giuseppe Bonaccorso
Author Profile Icon Giuseppe Bonaccorso
Giuseppe Bonaccorso
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Table of Contents (28) Chapters Close

Preface 1. Machine Learning Model Fundamentals 2. Loss Functions and Regularization FREE CHAPTER 3. Introduction to Semi-Supervised Learning 4. Advanced Semi-Supervised Classification 5. Graph-Based Semi-Supervised Learning 6. Clustering and Unsupervised Models 7. Advanced Clustering and Unsupervised Models 8. Clustering and Unsupervised Models for Marketing 9. Generalized Linear Models and Regression 10. Introduction to Time-Series Analysis 11. Bayesian Networks and Hidden Markov Models 12. The EM Algorithm 13. Component Analysis and Dimensionality Reduction 14. Hebbian Learning 15. Fundamentals of Ensemble Learning 16. Advanced Boosting Algorithms 17. Modeling Neural Networks 18. Optimizing Neural Networks 19. Deep Convolutional Networks 20. Recurrent Neural Networks 21. Autoencoders 22. Introduction to Generative Adversarial Networks 23. Deep Belief Networks 24. Introduction to Reinforcement Learning 25. Advanced Policy Estimation Algorithms 26. Other Books You May Enjoy
27. Index

Regularization

When a model is ill-conditioned or prone to overfitting, regularization offers some valid tools to mitigate the problems. From a mathematical viewpoint, a regularizer is a penalty added to the cost function, to impose an extra condition on the evolution of the parameters:

The parameter controls the strength of the regularization, which is expressed through the function . A fundamental condition on is that it must be differentiable so that the new composite cost function can still be optimized using SGD algorithms. In general, any regular function can be employed; however, we normally need a function that can contrast the indefinite growth of the parameters.

To understand the principle, let's consider the following diagram:

https://packt-type-cloud.s3.amazonaws.com/uploads/sites/3717/2019/05/IMG_49.png

Interpolation with a linear curve (left) and a parabolic one (right)

In the first diagram, the model is linear and has two parameters, while in the second one, it is quadratic and has three parameters. We already...

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Mastering Machine Learning Algorithms - Second Edition
Published in: Jan 2020
Publisher: Packt
ISBN-13: 9781838820299
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