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

Reducing bias in models    

In the current world, there are known, well-documented general biases based on gender, race, and sexual orientation. It means that the data we collect is expected to exhibit those biases unless we are dealing with an environment where an effort has been made to remove these biases before collecting the data.

All bias in algorithms is, directly or indirectly, due to human bias. Human bias can be reflected either in data used by the algorithm or in the formulation of the algorithm itself. For a typical machine learning project following the CRISP-DM (short for Cross-Industry Standard Process) lifecycle, which was explained in Chapter 5, Graph Algorithmsthe bias looks like the following:

The trickiest part of reducing bias is to first identify and locate unconscious bias. 

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