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

Using large-scale algorithms in cloud computing

The rapid growth of data and the increasing complexity of machine learning models have made distributed model training an essential component of modern deep learning pipelines. Large-scale algorithms demand vast amounts of computational resources and necessitate efficient parallelism to optimize their training times. Cloud computing offers an array of services and tools that facilitate distributed model training, allowing you to harness the full potential of resource-hungry, large-scale algorithms.

Some of the key advantages of using the Cloud for distributed model training include:

Scalability: The Cloud provides virtually unlimited resources, allowing you to scale your model training workloads to meet the demands of large-scale algorithms.
Flexibility: The Cloud supports a wide range of machine learning frameworks and libraries, enabling you to choose the most suitable tools for your specific needs.
Cost...