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

How Apache Spark empowers large-scale algorithm processing

Apache Spark has emerged as a leading platform for processing and analyzing big data, thanks to its powerful distributed computing capabilities, fault-tolerant nature, and ease of use. In this section, we will explore how Apache Spark empowers large-scale algorithm processing, making it an ideal choice for complex, resource-intensive tasks.

Distributed computing

At the core of Apache Spark’s architecture is the concept of data partitioning, which allows data to be divided across multiple nodes in a cluster. This feature enables parallel processing and efficient resource utilization, both of which are crucial for running large-scale algorithms. Spark’s architecture comprises a driver program and multiple executor processes distributed across worker nodes. The driver program is responsible for managing and distributing tasks across the executors, while each executor runs multiple tasks concurrently in separate...