Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
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

Arrow left icon
Product type Paperback
Published in Sep 2023
Publisher Packt
ISBN-13 9781803247762
Length 538 pages
Edition 2nd Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
Imran Ahmad Imran Ahmad
Author Profile Icon Imran Ahmad
Imran Ahmad
Arrow right icon
View More author details
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

Summary

In this chapter, we started by looking at the basics of supervised machine learning. Then, we looked at various classification algorithms in more detail. Next, we looked at different methods to evaluate the performance of classifiers and studied various regression algorithms. We also looked at the different methods that can be used to evaluate the performance of the algorithms that we studied.In the next chapter, we will look at neural networks and deep learning algorithms. We will look at the methods used to train a neural network and we will also look at the various tools and frameworks available for evaluating and deploying a neural network.

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime