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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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Toc

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

Further reading

  • Dempster A. P., Laird N. M., Rubin D. B., Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society, B, 39(1):1–38, 11/1977
  • Hansen F., Pedersen G. K. Jensen's Operator Inequality, arXiv:math/0204049 [math.OA]
  • Rubin D., Thayer D., EM algorithms for ML factor analysis, Psychometrika, 47/1982, Issue 1
  • Ghahramani Z., Hinton G. E., The EM algorithm for Mixtures of Factor Analyzers, CRC-TG-96-1, 05/1996
  • Hyvarinen A., Oja E., Independent Component Analysis: Algorithms and Applications, Neural Networks 13/2000
  • Luenberger D. G., Optimization by Vector Space Methods, Wiley, 1997
  • Ledoit O., Wolf M., A Well-Conditioned Estimator for Large-Dimensional Covariance Matrices, Journal of Multivariate Analysis, 88, 2/2004
  • Minka T.P., Automatic Choice of Dimensionality for PCA, NIPS 2000
  • Nasios N., Bors A. G., Variational Learning for Gaussian Mixture Models, IEEE Transactions on Systems...
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