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40 Algorithms Every Programmer Should Know

40 Algorithms Every Programmer Should Know

By : Imran Ahmad
3.8 (33)
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40 Algorithms Every Programmer Should Know

40 Algorithms Every Programmer Should Know

3.8 (33)
By: Imran Ahmad

Overview of this book

Algorithms have always played an important role in both the science and practice of computing. Beyond traditional computing, the ability to use algorithms to solve real-world problems is an important skill that any developer or programmer must have. This book will help you not only to develop the skills to select and use an algorithm to solve real-world problems 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, such as searching and sorting, with the help of practical examples. As you advance to a more complex set of algorithms, you'll learn about linear programming, page ranking, and graphs, and even work with machine learning algorithms, understanding the math and logic behind them. Further on, case studies such as weather prediction, tweet clustering, and movie recommendation engines will show you how to apply these algorithms optimally. 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 book, you'll have become adept at solving real-world computational problems by using a wide range of algorithms.
Table of Contents (19 chapters)
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1
Section 1: Fundamentals and Core Algorithms
7
Section 2: Machine Learning Algorithms
13
Section 3: Advanced Topics

Types of recommendation engines

Generally, there are three different types of recommendation engines:

  • Content-based recommendation engines

  • Collaborative filtering engines

  • Hybrid recommendation engines

Content-based recommendation engines

The basic idea of the content-based recommendation engine is to suggest items similar to those in which the user has preceding shown interest. The effectiveness of content-based recommendation engines is dependent on our ability to quantify the similarity of an item to others.

Let's look into the following diagram. If User 1 has read Doc 1, then we can recommend Doc 2 to the user, which is similar to Doc 1:

Now, the problem is how to determine which items are similar to each other...

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