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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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Concepts
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Authors (2):
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Giuseppe Bonaccorso Giuseppe Bonaccorso
Author Profile Icon Giuseppe Bonaccorso
Giuseppe Bonaccorso
Giuseppe Bonaccorso is an experienced manager in the fields of AI, data science, and machine learning. He has been involved in solution design, management, and delivery in different business contexts. He got his M.Sc.Eng in electronics in 2005 from the University of Catania, Italy, and continued his studies at the University of Rome Tor Vergata, Italy, and the University of Essex, UK. His main interests include machine/deep learning, reinforcement learning, big data, bio-inspired adaptive systems, neuroscience, and natural language processing.
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Giuseppe Bonaccorso Giuseppe Bonaccorso
Author Profile Icon Giuseppe Bonaccorso
Giuseppe Bonaccorso
LinkedIn
Giuseppe Bonaccorso is Head of Data Science in a large multinational company. He received his M.Sc.Eng. in Electronics in 2005 from University of Catania, Italy, and continued his studies at University of Rome Tor Vergata, and University of Essex, UK. His main interests include machine/deep learning, reinforcement learning, big data, and bio-inspired adaptive systems. He is author of several publications including Machine Learning Algorithms and Hands-On Unsupervised Learning with Python, published by Packt.
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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

Index

Symbols

Sanger's rule 416

A

activation function 500

activation function, Multilayer Perceptron (MLP)

about 509

hyperbolic tangent 509, 510

rectifier activation function 510, 511, 512

sigmoid 509, 510

softmax 512

AdaBoost

about 456, 457, 458, 459, 460

example, with scikit-learn 468, 469, 470, 471, 473

AdaBoost.M1 456

AdaBoost.R2 465, 466, 467, 468

AdaBoost.SAMME 460, 461

AdaBoost.SAMME.R 462, 463, 464

AdaDelta

about 539, 540

using, with TensorFlow/Keras 540

AdaGrad

using, with TensorFlow/Keras 538

Adaptive Moment Estimation (Adam)

about 536

in TensorFlow/Keras 537

adjacency matrix 134

adjusted Rand index

about 197, 198

adversarial training

about 635, 636, 637, 638, 639

affinity matrix 134

AIC

used, for determining optimal number of components 366, 367

anti-Hebbian 423

approaches, ensemble learning

bagging (bootstrap aggregating) 441

boosting...

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Authors (2)

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Profile icon Bonaccorso
Bonaccorso
LinkedIn icon Github icon
Giuseppe Bonaccorso is Head of Data Science in a large multinational company. He received his M.Sc.Eng. in Electronics in 2005 from University of Catania, Italy, and continued his studies at University of Rome Tor Vergata, and University of Essex, UK. His main interests include machine/deep learning, reinforcement learning, big data, and bio-inspired adaptive systems. He is author of several publications including Machine Learning Algorithms and Hands-On Unsupervised Learning with Python, published by Packt.
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See other products by Bonaccorso
Profile icon Giuseppe Bonaccorso
Giuseppe Bonaccorso
Giuseppe Bonaccorso is an experienced manager in the fields of AI, data science, and machine learning. He has been involved in solution design, management, and delivery in different business contexts. He got his M.Sc.Eng in electronics in 2005 from the University of Catania, Italy, and continued his studies at the University of Rome Tor Vergata, Italy, and the University of Essex, UK. His main interests include machine/deep learning, reinforcement learning, big data, bio-inspired adaptive systems, neuroscience, and natural language processing.
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