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

Recurrent networks

All the neural network models that we analyzed in the previous chapter have a common feature. Once the training process is completed, the weights are frozen, and the output depends only on the input sample. Clearly, this is the expected behavior of a classifier, but there are many scenarios where a prediction must take into account the history of the input values. A time series is a classic example (review Chapter 10, Introduction to Time-Series Analysis, for further details). Let's suppose that we need to predict the temperature for the next week. If we try to use only the last known x(t) value and an MLP trained to predict x(t + 1), it's impossible to take into account temporal conditions, such as the season, the history of the season over the years, the position in the season, and so on.

The regressor will be able to associate the output that yields the minimum average error but, in real-life situations, this isn't enough. The only reasonable...