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

The evolution of advanced sequential modeling techniques

In Chapter 10, Understanding Sequential Models, we touched upon the foundational aspects of sequential models. While they serve numerous use cases, they face challenges in grasping and producing the complex intricacies of human language.

We’ll begin our journey by discussing autoencoders. Introduced in the early 2010s, autoencoders provided a refreshing approach to data representation. They marked a significant evolution in natural language processing (NLP), transforming how we thought about data encoding and decoding. But the momentum in NLP didn’t stop there. By the mid-2010s, Seq2Seq models entered the scene, bringing forth innovative methodologies for tasks such as language translation. These models could adeptly transform one sequence form into another, heralding an era of advanced sequence processing.

However, with the rise in data complexity, the NLP community felt the need for more sophisticated...