Book Image

50 Algorithms Every Programmer Should Know - Second Edition

By : Imran Ahmad
4 (5)
Book Image

50 Algorithms Every Programmer Should Know - Second Edition

4 (5)
By: Imran Ahmad

Overview of this book

The ability to use algorithms to solve real-world problems is a must-have skill for any developer or programmer. This book will help you not only to develop the skills to select and use an algorithm to tackle problems in the real world 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, with the help of practical examples. As you advance, you'll learn about linear programming, page ranking, and graphs, and will then work with machine learning algorithms to understand the math and logic behind them. Case studies will show you how to apply these algorithms optimally before you focus on deep learning algorithms and learn about different types of deep learning models along with their practical use. You will also learn about modern sequential models and their variants, algorithms, methodologies, and architectures that are used to implement Large Language Models (LLMs) such as ChatGPT. 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 programming book, you'll have become adept at solving real-world computational problems by using a wide range of algorithms.
Table of Contents (22 chapters)
Free Chapter
1
Section 1: Fundamentals and Core Algorithms
7
Section 2: Machine Learning Algorithms
14
Section 3: Advanced Topics
20
Other Books You May Enjoy
21
Index

Challenges facing algorithmic solutions

In addition to designing, developing, and testing an algorithm, in many cases, it is important to consider certain practical aspects of starting to rely on a machine to solve a real-world problem. For certain algorithms, we may need to consider ways to reliably incorporate new, important information that is expected to keep changing even after we have deployed our algorithm. For example, the unexpected disruption of global supply chains may negate some of the assumptions we used to train a model predicting the profit margins for a product. We need to carefully consider whether incorporating this new information will change the quality of our well-tested algorithm in any way. If so, how is our design going to handle it?

Expecting the unexpected

Most solutions to real-world problems developed using algorithms are based on some assumptions. These assumptions may unexpectedly change after the model has been deployed. Some algorithms use...