You're reading from Mastering Machine Learning Algorithms Expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work

Product type Paperback

Published in Jan 2020

Publisher Packt

ISBN-13 9781838820299

Length 798 pages

Edition 2nd Edition

Languages

Python

Tools

Keras

Concepts

Machine Learning

Authors (2):

Giuseppe Bonaccorso

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Table of Contents (28) Chapters

Preface

1. Machine Learning Model Fundamentals

2. Loss Functions and Regularization FREE CHAPTER

3. Introduction to Semi-Supervised Learning

4. Advanced Semi-Supervised Classification

5. Graph-Based Semi-Supervised Learning

6. Clustering and Unsupervised Models

7. Advanced Clustering and Unsupervised Models

8. Clustering and Unsupervised Models for Marketing

9. Generalized Linear Models and Regression

10. Introduction to Time-Series Analysis

11. Bayesian Networks and Hidden Markov Models

12. The EM Algorithm

13. Component Analysis and Dimensionality Reduction

14. Hebbian Learning

15. Fundamentals of Ensemble Learning

16. Advanced Boosting Algorithms

17. Modeling Neural Networks

18. Optimizing Neural Networks

19. Deep Convolutional Networks

20. Recurrent Neural Networks

21. Autoencoders

22. Introduction to Generative Adversarial Networks

23. Deep Belief Networks

24. Introduction to Reinforcement Learning

25. Advanced Policy Estimation Algorithms

26. Other Books You May Enjoy

27. 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...