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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27
Index

Summary

In this chapter, we introduced the concept of RNNs, emphasizing the issues that normally arise when classic models are trained using the BPTT algorithm. In particular, we explained why these networks cannot easily learn long-term dependencies.

For this reason, new models have been proposed, whose performance was immediately outstanding. We discussed the most famous recurrent cell, called Long Short-Term Memory (LSTM), which can be used in layers that can easily learn all the most important dependencies of a sequence, allowing us to minimize the prediction error even in contexts with very high variance (such as stock market quotations). The last topic was a simplified version of the idea implemented in LSTMs, which led to a model called a Gated Recurrent Unit (GRU). This cell is simpler and more computationally efficient, and many benchmarks confirmed that its performance is approximately the same as LSTM.

In the next chapter, we are going to discuss some models called...