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50 Algorithms Every Programmer Should Know - Second Edition

You're reading from  50 Algorithms Every Programmer Should Know - Second Edition

Product type Book
Published in Sep 2023
Publisher Packt
ISBN-13 9781803247762
Pages 538 pages
Edition 2nd Edition
Languages
Concepts
Author (1):
Imran Ahmad Imran Ahmad
Profile icon Imran Ahmad
LinkedIn
Imran Ahmad has been a part of cutting-edge research about algorithms and machine learning for many years. He completed his PhD in 2010, in which he proposed a new linear programming-based algorithm that can be used to optimally assign resources in a large-scale cloud computing environment. In 2017, Imran developed a real-time analytics framework named StreamSensing. He has since authored multiple research papers that use StreamSensing to process multimedia data for various machine learning algorithms. Imran is currently working at Advanced Analytics Solution Center (A2SC) at the Canadian Federal Government as a data scientist. He is using machine learning algorithms for critical use cases. Imran is a visiting professor at Carleton University, Ottawa. He has also been teaching for Google and Learning Tree for the last few years.
See other products by Imran Ahmad

Table of Contents (22) Chapters

Preface 1. Section 1: Fundamentals and Core Algorithms
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

Areas of practical applications

Recommendation systems play a pivotal role in our daily digital interactions. To truly understand their significance, let’s delve into their applications across various industries.

Based on the comprehensive details provided about Netflix’s use of data science and its recommendation system, let’s look at the restructured statement addressing the points mentioned.

Netflix’s mastery of data-driven recommendations

Netflix, a leader in streaming, has harnessed data analytics to fine-tune content recommendations, with 800 engineers in Silicon Valley advancing this effort. Their emphasis on data-driven strategies is evident in the Netflix Prize challenge. The winning team, BellKor’s Pragmatic Chaos, used 107 diverse algorithms, from matrix factorization to restricted Boltzman machines, investing 2,000 hours in its development.

The results were a significant 10.06% improvement in their “Cinematch”...

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Authors (1)

Profile icon Imran Ahmad
LinkedIn
Imran Ahmad has been a part of cutting-edge research about algorithms and machine learning for many years. He completed his PhD in 2010, in which he proposed a new linear programming-based algorithm that can be used to optimally assign resources in a large-scale cloud computing environment. In 2017, Imran developed a real-time analytics framework named StreamSensing. He has since authored multiple research papers that use StreamSensing to process multimedia data for various machine learning algorithms. Imran is currently working at Advanced Analytics Solution Center (A2SC) at the Canadian Federal Government as a data scientist. He is using machine learning algorithms for critical use cases. Imran is a visiting professor at Carleton University, Ottawa. He has also been teaching for Google and Learning Tree for the last few years.
Read more
See other products by Imran Ahmad

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