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)
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Index

Long Short-Term Memory (LSTM)

This model (which represents the state-of-the-art recurrent cell in many fields) was proposed in 1997 by Hochreiter and Schmidhuber (in Hochreiter S., Schmidhuber J., Long Short-Term Memory, Neural Computation, Vol. 9, 11/1997) with the emblematic name Long Short-Term Memory (LSTM). As the name suggests, the idea is to create a more complex artificial recurrent neuron that can be plugged into larger networks and trained without the risk of vanishing and, of course, exploding gradients. One of the key elements of classic recurrent networks is that they are focused on learning, but not on selectively forgetting. This ability is indeed necessary for optimizing the memory in order to remember what is really important and removing all those pieces of information that are not necessary to predict new values.

To achieve this goal, LSTM exploits two important features (it's helpful to discuss them before moving on to the model). The first one is an explicit...