You're reading from 50 Algorithms Every Programmer Should Know Tackle computer science challenges with classic to modern algorithms in machine learning, software design, data systems, and cryptography

Product type Paperback

Published in Sep 2023

Publisher Packt

ISBN-13 9781803247762

Length 538 pages

Edition 2nd Edition

Languages

Processing

Tools

Processing

Concepts

Data Structures and Algorithms

Author (1):

Imran Ahmad

View More author details

Table of Contents (22) Chapters

Preface

1. Section 1: Fundamentals and Core Algorithms FREE CHAPTER

2. Overview of Algorithms

3. Data Structures Used in Algorithms

4. Sorting and Searching Algorithms

5. Designing Algorithms

6. Graph Algorithms

7. Section 2: Machine Learning Algorithms

8. Unsupervised Machine Learning Algorithms

9. Traditional Supervised Learning Algorithms

10. Neural Network Algorithms

11. Algorithms for Natural Language Processing

12. Understanding Sequential Models

13. Advanced Sequential Modeling Algorithms

14. Section 3: Advanced Topics

15. Recommendation Engines

16. Algorithmic Strategies for Data Handling

17. Cryptography

18. Large-Scale Algorithms

19. Practical Considerations

20. Other Books You May Enjoy

21. Index

Understanding sequential data

Sequential data is a specific type of data structure where the order of the elements matters, and each element has a relational dependency on its predecessors. This “sequential behavior” is distinct because it conveys information not just in the individual elements but also in the pattern or sequence in which they occur. In sequential data, the current observation is not only influenced by external factors but also by previous observations in the sequence. This dependency forms the core characteristic of sequential data.

Understanding the different types of sequential data is essential to appreciate its broad applications. Here are the primary categories:

Time series data: This is a series of data points indexed or listed in time order. The value at any point in time is dependent on the past values. Time series data is widely used in various fields, including economics, finance, and healthcare.
Textual data: Text data...