Book Image

50 Algorithms Every Programmer Should Know - Second Edition

By : Imran Ahmad
4 (5)
Book Image

50 Algorithms Every Programmer Should Know - Second Edition

4 (5)
By: Imran Ahmad

Overview of this book

The ability to use algorithms to solve real-world problems is a must-have skill for any developer or programmer. This book will help you not only to develop the skills to select and use an algorithm to tackle problems in the real world but also to understand how it works. You'll start with an introduction to algorithms and discover various algorithm design techniques, before exploring how to implement different types of algorithms, with the help of practical examples. As you advance, you'll learn about linear programming, page ranking, and graphs, and will then work with machine learning algorithms to understand the math and logic behind them. Case studies will show you how to apply these algorithms optimally before you focus on deep learning algorithms and learn about different types of deep learning models along with their practical use. You will also learn about modern sequential models and their variants, algorithms, methodologies, and architectures that are used to implement Large Language Models (LLMs) such as ChatGPT. Finally, you'll become well versed in techniques that enable parallel processing, giving you the ability to use these algorithms for compute-intensive tasks. By the end of this programming book, you'll have become adept at solving real-world computational problems by using a wide range of algorithms.
Table of Contents (22 chapters)
Free Chapter
1
Section 1: Fundamentals and Core Algorithms
7
Section 2: Machine Learning Algorithms
14
Section 3: Advanced Topics
20
Other Books You May Enjoy
21
Index

Recommendation Engines

The best recommendation I can have is my own talents, and the fruits of my own labors, and what others will not do for me, I will try and do for myself.

—18–19th-century scientist John James Audubon

Recommendation engines harness the power of available data on user preferences and item details to offer tailored suggestions. At their core, these engines aim to identify commonalities among various items and understand the dynamics of user-item interactions. Rather than just focusing on products, recommendation systems cast a wider net, considering any type of item – be it a song, a news article, or a product – and tailoring their suggestions accordingly.

This chapter starts by presenting the basics of recommendation engines. Then, it discusses various types of recommendation engines. In the subsequent sections of this chapter, we’ll explore the inner workings of recommendation systems. These systems...