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

Summary

The foundational concepts of sequential models were explained in this chapter, which aimed to give you a basic understanding of the techniques and methodologies of such techniques. In this chapter, we presented RNNs, which are great for handling sequential data. A GRU is a type of RNN that was introduced by Cho et al. in 2014 as a simpler alternative to LSTM networks.

Like LSTMs, GRUs are designed to learn long-term dependencies in sequential data, but they do so using a different approach. GRUs use a single gating mechanism to control the flow of information into and out of the hidden state, rather than the three gates used by LSTMs. This makes them easier to train and requires fewer parameters, making them more efficient to use.

The next chapter introduces some advanced techniques related to sequential models.