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50 Algorithms Every Programmer Should Know

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

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Product type Paperback
Published in Sep 2023
Publisher Packt
ISBN-13 9781803247762
Length 538 pages
Edition 2nd Edition
Languages
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Author (1):
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Imran Ahmad Imran Ahmad
Author Profile Icon Imran Ahmad
Imran Ahmad
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Toc

Table of Contents (22) Chapters Close

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 theoretical limitations of parallel computing

It is important to note that parallel algorithms are not a silver bullet. Even the best-designed parallel architectures may not give the performance that we may expect. The complexities of parallel computing, such as communication overhead and synchronization, make it challenging to achieve optimal efficiency. One law that has been developed to help navigate these complexities and better understand the potential gains and limitations of parallel algorithms is Amdahl’s law.

Amdahl’s law

Gene Amdahl was one of the first people to study parallel processing in the 1960s. He proposed Amdahl’s law, which is still applicable today and is a basis on which to understand the various trade-offs involved when designing a parallel computing solution. Amdahl’s law provides a theoretical limit on the maximum improvement in execution time that can be achieved with a parallelized version of an algorithm...

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