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

Product type Paperback

Published in Jan 2020

Publisher Packt

ISBN-13 9781838820299

Length 798 pages

Edition 2nd Edition

Languages

Python

Tools

Keras

Concepts

Machine Learning

Authors (2):

Giuseppe Bonaccorso

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Table of Contents (28) Chapters

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

Generative Gaussian Mixture

The first model we're going to discuss is called Generative Gaussian Mixture, and it aims to model the data generating process p_data using a sum of weighted Gaussian distributions. Since the model is generative, its structure allows us not only to cluster the existing dataset into well-defined regions (represented as Gaussians), but also to output the probability of any new data point to belong to each of the classes. This model is very flexible, and can be applied to solve all those problems where it's necessary to perform a clustering and a classification at the same time, obtaining the assignment probability vector that determines the likelihood of a data point to be generated by a specific Gaussian distribution.

Generative Gaussian Mixture theory

Generative Gaussian Mixture is an inductive algorithm for semi-supervised classification and clustering that's aimed at modeling the conditional probability given both a...