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Mastering Machine Learning Algorithms. - Second Edition
Mastering Machine Learning Algorithms. - Second Edition

Mastering Machine Learning Algorithms.: Expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work, Second Edition

By Giuseppe Bonaccorso
$39.99 $9.99
Book Jan 2020 798 pages 2nd Edition
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Product Details


Publication date : Jan 31, 2020
Length 798 pages
Edition : 2nd Edition
Language : English
ISBN-13 : 9781838820299
Vendor :
Google
Category :
Languages :
Table of content icon View table of contents Preview book icon Preview Book

Mastering Machine Learning Algorithms. - Second Edition

Loss Functions and Regularization

Loss functions are proxies that allow us to measure the error made by a machine learning model. They define the very structure of the problem to solve, and prepare the algorithm for an optimization step aimed at maximizing or minimizing the loss function. Through this process, we make sure that all our parameters are chosen in order to reduce the error as much as possible. In this chapter, we're going to discuss the fundamental loss functions and their properties. I've also included a dedicated section about the concept of regularization; regularized models are more resilient to overfitting, and can achieve results beyond the limits of a simple loss function.

In particular, we'll discuss:

  • Defining loss and cost functions
  • Examples of cost functions, including mean squared error and the Huber and hinge cost functions
  • Regularization
  • Examples of regularization, including Ridge, Lasso, ElasticNet, and early...

Defining loss and cost functions

Many machine learning problems can be expressed throughout a proxy function that measures the training error. The obvious implicit assumption is that, by reducing both training and validation errors, the accuracy increases, and the algorithm reaches its objective.

If we consider a supervised scenario (many considerations hold also for semi-supervised ones), with finite datasets X and Y:

We can define the generic loss function for a single data point as:

J is a function of the whole parameter set and must be proportional to the error between the true label and the predicted label.

A very important property of a loss function is convexity. In many real cases, this is an almost impossible condition; however, it's always useful to look for convex loss functions, because they can be easily optimized through the gradient descent method. We're going to discuss this topic in Chapter 10, Introduction...

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...

Summary

In this chapter, we introduced the loss and cost functions, first as proxies of the expected risk, and then we detailed some common situations that can be experienced during an optimization problem. We also exposed some common cost functions, together with their main features and specific applications.

In the last part, we discussed regularization, explaining how it can mitigate the effects of overfitting and induce sparsity. In particular, the employment of Lasso can help the data scientist to perform automatic feature selection by forcing all secondary coefficients to become equal to 0.

In the next chapter, Chapter 3, Introduction to Semi-Supervised Learning, we're going to introduce semi-supervised learning, focusing our attention on the concepts of transductive and inductive learning.

Further reading

  • Darwiche A., Human-Level Intelligence or Animal-Like Abilities?, Communications of the ACM, Vol. 61, 10/2018
  • Crammer K., Kearns M., Wortman J., Learning from Multiple Sources, Journal of Machine Learning Research, 9/2008
  • Mohri M., Rostamizadeh A., Talwalkar A., Foundations of Machine Learning, Second edition, The MIT Press, 2018
  • Valiant L., A theory of the learnable, Communications of the ACM, 27, 1984
  • Ng A. Y., Feature selection, L1 vs. L2 regularization, and rotational invariance, ICML, 2004
  • Dube S., High Dimensional Spaces, Deep Learning and Adversarial Examples, arXiv:1801.00634 [cs.CV]
  • Sra S., Nowozin S., Wright S. J. (edited by), Optimization for Machine Learning, The MIT Press, 2011
  • Bonaccorso G., Machine Learning Algorithms, Second Edition, Packt, 2018
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Key benefits

  • Updated to include new algorithms and techniques
  • Code updated to Python 3.8 & TensorFlow 2.x
  • New coverage of regression analysis, time series analysis, deep learning models, and cutting-edge applications

Description

Mastering Machine Learning Algorithms, Second Edition helps you harness the real power of machine learning algorithms in order to implement smarter ways of meeting today's overwhelming data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement learning, supervised learning, and unsupervised learning domains. You will use all the modern libraries from the Python ecosystem – including NumPy and Keras – to extract features from varied complexities of data. Ranging from Bayesian models to the Markov chain Monte Carlo algorithm to Hidden Markov models, this machine learning book teaches you how to extract features from your dataset, perform complex dimensionality reduction, and train supervised and semi-supervised models by making use of Python-based libraries such as scikit-learn. You will also discover practical applications for complex techniques such as maximum likelihood estimation, Hebbian learning, and ensemble learning, and how to use TensorFlow 2.x to train effective deep neural networks. By the end of this book, you will be ready to implement and solve end-to-end machine learning problems and use case scenarios.

What you will learn

Understand the characteristics of a machine learning algorithm Implement algorithms from supervised, semi-supervised, unsupervised, and RL domains Learn how regression works in time-series analysis and risk prediction Create, model, and train complex probabilistic models Cluster high-dimensional data and evaluate model accuracy Discover how artificial neural networks work – train, optimize, and validate them Work with autoencoders, Hebbian networks, and GANs

What do you get with eBook?

Product feature icon Instant access to your Digital eBook purchase
Product feature icon Download this book in EPUB and PDF formats
Product feature icon Access this title in our online reader with advanced features
Product feature icon DRM FREE - Read whenever, wherever and however you want
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Product Details


Publication date : Jan 31, 2020
Length 798 pages
Edition : 2nd Edition
Language : English
ISBN-13 : 9781838820299
Vendor :
Google
Category :
Languages :

Table of Contents

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

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