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

Example – Security Concerns When Deploying a Machine Learning Model

In Chapter 6Unsupervised Machine Learning Algorithms, we looked at the CRISP-DM (Cross-Industry Standard Process for Data Mining) life cycle, which specifies the different phases of training and deploying a machine learning model. Once a model is trained and evaluated, the final phase is deployment. If it is a critical machine learning model, then we want to make sure that all of its security goals are met.

Let's analyze the common challenges faced in deploying a model such as this and how we can address those challenges using the concepts discussed in this chapter. We will discuss strategies to protect our trained model against the following three challenges:

  • Man-in-the-Middle (MITM) attacks

  • Masquerading

  • Data tempering

Let's look at them one by one.

...