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Mastering Machine Learning Algorithms

You're reading from   Mastering Machine Learning Algorithms Expert techniques to implement popular machine learning algorithms and fine-tune your models

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Product type Paperback
Published in May 2018
Publisher Packt
ISBN-13 9781788621113
Length 576 pages
Edition 1st Edition
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Author (1):
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Giuseppe Bonaccorso Giuseppe Bonaccorso
Author Profile Icon Giuseppe Bonaccorso
Giuseppe Bonaccorso
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Table of Contents (17) Chapters Close

Preface 1. Machine Learning Model Fundamentals FREE CHAPTER 2. Introduction to Semi-Supervised Learning 3. Graph-Based Semi-Supervised Learning 4. Bayesian Networks and Hidden Markov Models 5. EM Algorithm and Applications 6. Hebbian Learning and Self-Organizing Maps 7. Clustering Algorithms 8. Ensemble Learning 9. Neural Networks for Machine Learning 10. Advanced Neural Models 11. Autoencoders 12. Generative Adversarial Networks 13. Deep Belief Networks 14. Introduction to Reinforcement Learning 15. Advanced Policy Estimation Algorithms 16. Other Books You May Enjoy

Value iteration

An alternative approach to policy iteration is provided by the value iteration algorithm. The main assumption is based on the empirical observation that the policy evaluation step converges rather quickly and it's reasonable to stop the process after a fixed number of steps (normally 1). In fact, policy iteration can be imagined like a game where the first player tries to find the correct values considering a stable policy, while the other one creates a new policy that is greedy with respect to the new values. Clearly, the second step compromises the validity of the previous evaluation, forcing the first player to repeat the process. However, as the Bellman equation uses a single fixed point, the algorithm converges to a solution characterized by the fact that the policy doesn't change anymore and, consequently, the evaluation becomes stable. This process...

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