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40 Algorithms Every Programmer Should Know

You're reading from 40 Algorithms Every Programmer Should Know

Product type Book

Published in Jun 2020

Publisher Packt

ISBN-13 9781789801217

Pages 382 pages

Edition 1st Edition

Languages

Concepts

Data Structures and Algorithms

Author (1):

Imran Ahmad

Table of Contents (19) 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. Recommendation Engines

13. Section 3: Advanced Topics

14. Data Algorithms

15. Cryptography

16. Large-Scale Algorithms

17. Practical Considerations

18. Other Books You May Enjoy

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Presenting data storage algorithms

A reliable and efficient data repository is the heart of a distributed system. If this data repository is created for analytics, then it is also called a data lake. A data repository brings together data from different domains into a single location. Let's start with first understanding different issues related to the storage of data in a distributed repository.

Understanding data storage strategies

In the initial years of digital computing, the usual way of designing a data repository was by using a single node architecture. With the ever-increasing sizes of datasets, distributed storage of data has now become mainstream. The right strategy to store data in a distributed environment...

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Authors (1)

Imran Ahmad

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.

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