Book Image

Mastering Machine Learning Algorithms. - Second Edition

By : Giuseppe Bonaccorso
Book Image

Mastering Machine Learning Algorithms. - Second Edition

By: Giuseppe Bonaccorso

Overview of this book

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.
Table of Contents (28 chapters)
26
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Index

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...