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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

Summary

In this chapter, we journeyed through the evolution of neural networks, examining different types, key components like activation functions, and the significant gradient descent algorithm. We touched upon the concept of transfer learning and its practical application in identifying fraudulent documents.

As we proceed to the next chapter, we’ll delve into natural language processing, exploring areas such as word embedding and recurrent networks. We will also learn how to implement sentiment analysis. The captivating realm of neural networks continues to unfold.

Learn more on Discord

To join the Discord community for this book – where you can share feedback, ask questions to the author, and learn about new releases – follow the QR code below:

https://packt.link/WHLel

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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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