You're reading from Python Machine Learning, Second Edition Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow

Product type Paperback

Published in Sep 2017

Publisher Packt

ISBN-13 9781787125933

Length 622 pages

Edition 2nd Edition

Languages

Python

Tools

Scikit-learn

Concepts

Deep Learning

Authors (2):

Vahid Mirjalili

Sebastian Raschka

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Table of Contents (18) Chapters

Preface

1. Giving Computers the Ability to Learn from Data

2. Training Simple Machine Learning Algorithms for Classification FREE CHAPTER

3. A Tour of Machine Learning Classifiers Using scikit-learn

4. Building Good Training Sets – Data Preprocessing

5. Compressing Data via Dimensionality Reduction

6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning

7. Combining Different Models for Ensemble Learning

8. Applying Machine Learning to Sentiment Analysis

9. Embedding a Machine Learning Model into a Web Application

10. Predicting Continuous Target Variables with Regression Analysis

11. Working with Unlabeled Data – Clustering Analysis

12. Implementing a Multilayer Artificial Neural Network from Scratch

13. Parallelizing Neural Network Training with TensorFlow

14. Going Deeper – The Mechanics of TensorFlow

15. Classifying Images with Deep Convolutional Neural Networks

16. Modeling Sequential Data Using Recurrent Neural Networks

Index

Introducing sequential data

Let's begin our discussion of RNNs by looking at the nature of sequential data, more commonly known as sequences. We'll take a look at the unique properties of sequences that make them different from other kinds of data. We'll then see how we can represent sequential data, and explore the various categories of models for sequential data, which are based on the input and output of a model. This will help us explore the relationship between RNNs and sequences a little bit later on in the chapter.

Modeling sequential data – order matters

What makes sequences unique, from other data types, is that elements in a sequence appear in a certain order, and are not independent of each other.

If you recall from Chapter 6, Learning Best Practices for Model Evaluation and Hyperparameter Tuning, we discussed that typical machine learning algorithms for supervised learning assume that the input data is Independent and Identically Distributed (IID). For example...