Book Image

40 Algorithms Every Programmer Should Know

By : Imran Ahmad
5 (2)
Book Image

40 Algorithms Every Programmer Should Know

5 (2)
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)
1
Section 1: Fundamentals and Core Algorithms
7
Section 2: Machine Learning Algorithms
13
Section 3: Advanced Topics

Understanding graph traversals

To make use of graphs, information needs to be mined from them. Graph traversal is defined as the strategy used to make sure that every vertex and edge is visited in an orderly manner. An effort is made to make sure that each vertex and edge is visited exactly once; no more and no less. Broadly, there can be two different ways of traveling a graph to search the data in it. Going by breadth is called breadth-first search (BFS) and going by depth is called depth-first search (DFS). Let's look at them one by one.

Breadth-first search 

BFS works best when there is a concept of layers or levels of neighborhoods in the aGraph we are dealing with. For example, when the connections of...