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

Presenting streaming data algorithms

Data can be categorized as bounded or unbounded. Bounded data is data at rest and is usually processed through a batch process. Streaming is basically data processing on unbounded data. Let's look into an example. Let's assume that we are analyzing fraudulent transactions at a bank. If we want to look for fraud transactions 7 days ago, we have to look at the data at rest; this is an example of a batch process.

n the other hand, if we want to detect fraud in real-time, that is an example of streaming. Hence, streaming data algorithms are those algorithms that deal with processing data streams. The fundamental idea is to divide the input data stream into batches, which are then processed by the processing node. Streaming algorithms need to be fault-tolerant and should be able to handle the incoming velocity of data...