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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

Understanding the limitations of recommender systems

Recommendation engines use predictive algorithms to suggest recommendations to a bunch of users. It is a powerful technology, but we should be aware of its limitations. Let's look into the various limitations of recommendation systems.

The cold start problem

It is obvious that, for collaborative filtering to work, we need to have historical data about user preferences. For a new user, we may not have any data, so our user similarity algorithm will be based on assumptions that may not be accurate. For content-based recommendations, we may not have the details about the new items right away. This requirement of having data about items and users to generate high-quality...

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