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50 Algorithms Every Programmer Should Know
50 Algorithms Every Programmer Should Know

50 Algorithms Every Programmer Should Know: Tackle computer science challenges with classic to modern algorithms in machine learning, software design, data systems, and cryptography , Second Edition

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50 Algorithms Every Programmer Should Know

Section 1: Fundamentals and Core Algorithms

This section introduces the core aspects of algorithms. We will explore what an algorithm is and how to design it. We will also learn about the data structures used in algorithms. This section also introduces sorting and searching algorithms along with algorithms to solve graphical problems. The chapters included in this section are:

  • Chapter 1Overview of Algorithms
  • Chapter 2Data Structures used in Algorithms
  • Chapter 3Sorting and Searching Algorithms
  • Chapter 4Designing Algorithms
  • Chapter 5Graph Algorithms

What is an algorithm?

In the simplest terms, an algorithm is a set of rules for carrying out some calculations to solve a problem. It is designed to yield results for any valid input according to precisely defined instructions. If you look up the word algorithm in a dictionary (such as American Heritage), it defines the concept as follows:

An algorithm is a finite set of unambiguous instructions that, given some set of initial conditions, can be performed in a prescribed sequence to achieve a certain goal and that has a recognizable set of end conditions.

Designing an algorithm is an effort to create a mathematical recipe in the most efficient way that can effectively be used to solve a real-world problem. This recipe may be used as the basis for developing a more reusable and generic mathematical solution that can be applied to a wider set of similar problems.

The phases of an algorithm

The different phases of developing, deploying, and finally, using...

Python packages

Python is a general-purpose language. It follows the philosophy of “batteries included,” which means that there is a standard library that is available, without making the user download separate packages. However, the standard library modules only provide the bare minimum functionality. Based on the specific use case you are working on, additional packages may need to be installed. The official third-party repository for Python packages is called PyPI, which stands for Python Package Index. It hosts Python packages both as source distribution and pre-compiled code. Currently, there are more than 113,000 Python packages hosted at PyPI. The easiest way to install additional packages is through the pip package management system. pip is a nerdy recursive acronym, which are abundant in Python culture. pip stands for Pip Installs Python. The good news is that starting from version 3.4 of Python, pip is installed by default. To check the version of pip, you...

Algorithm design techniques

An algorithm is a mathematical solution to a real-world problem. When designing an algorithm, we keep the following three design concerns in mind as we work on designing and fine-tuning the algorithms:

  • Concern 1: Is this algorithm producing the result we expected?
  • Concern 2: Is this the most optimal way to get these results?
  • Concern 3: How is the algorithm going to perform on larger datasets?

It is important to understand the complexity of the problem itself before designing a solution for it. For example, it helps us to design an appropriate solution if we characterize the problem in terms of its needs and complexity.

Generally, the algorithms can be divided into the following types based on the characteristics of the problem:

  • Data-intensive algorithms: Data-intensive algorithms are designed to deal with a large amount of data. They are expected to have relatively simplistic processing requirements. A compression...

Performance analysis

Analyzing the performance of an algorithm is an important part of its design. One of the ways to estimate the performance of an algorithm is to analyze its complexity.

Complexity theory is the study of how complicated algorithms are. To be useful, any algorithm should have three key features:

  • Should be correct: A good algorithm should produce the correct result. To confirm that an algorithm is working correctly, it needs to be extensively tested, especially testing edge cases.
  • Should be understandable: A good algorithm should be understandable. The best algorithm in the world is not very useful if it’s too complicated for us to implement on a computer.
  • Should be efficient: A good algorithm should be efficient. Even if an algorithm produces the correct result, it won’t help us much if it takes a thousand years or if it requires 1 billion terabytes of memory.

There are two possible types of analysis to quantify the...

Selecting an algorithm

How do you know which one is a better solution? How do you know which algorithm runs faster? Analyzing the time complexity of an algorithm may answer these types of questions.

To see where it can be useful, let’s take a simple example where the objective is to sort a list of numbers. There are a bunch of algorithms readily available that can do the job. The issue is how to choose the right one.

First, an observation that can be made is that if there are not too many numbers in the list, then it does not matter which algorithm we choose to sort the list of numbers. So, if there are only 10 numbers in the list (n=10), then it does not matter which algorithm we choose as it would probably not take more than a few microseconds, even with a very simple algorithm. But as n increases, the choice of the right algorithm starts to make a difference. A poorly designed algorithm may take a couple of hours to run, while a well-designed algorithm may finish...

Validating an algorithm

Validating an algorithm confirms that it is actually providing a mathematical solution to the problem we are trying to solve. A validation process should check the results for as many possible values and types of input values as possible.

Exact, approximate, and randomized algorithms

Validating an algorithm also depends on the type of the algorithm as the testing techniques are different. Let’s first differentiate between deterministic and randomized algorithms.

For deterministic algorithms, a particular input always generates exactly the same output. But for certain classes of algorithms, a sequence of random numbers is also taken as input, which makes the output different each time the algorithm is run. The k-means clustering algorithm, which is detailed in Chapter 6, Unsupervised Machine Learning Algorithms, is an example of such an algorithm:

Figure 1.4: Deterministic and Randomized Algorithms

Algorithms can also be divided...

Summary

This chapter was about learning the basics of algorithms. First, we learned about the different phases of developing an algorithm. We discussed the different ways of specifying the logic of an algorithm that are necessary for designing it. Then, we looked at how to design an algorithm. We learned two different ways of analyzing the performance of an algorithm. Finally, we studied different aspects of validating an algorithm.

After going through this chapter, we should be able to understand the pseudocode of an algorithm. We should understand the different phases in developing and deploying an algorithm. We also learned how to use Big O notation to evaluate the performance of an algorithm.

The next chapter is about the data structures used in algorithms. We will start by looking at the data structures available in Python. We will then look at how we can use these data structures to create more sophisticated data structures, such as stacks, queues, and trees, which are...

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Key benefits

  • Familiarize yourself with advanced deep learning architectures
  • Explore newer topics, such as handling hidden bias in data and algorithm explainability
  • Get to grips with different programming algorithms and choose the right data structures for their optimal implementation

Description

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.

Who is this book for?

This computer science book is for programmers or developers who want to understand the use of algorithms for problem-solving and writing efficient code. Whether you are a beginner looking to learn the most used algorithms concisely or an experienced programmer looking to explore cutting-edge algorithms in data science, machine learning, and cryptography, you'll find this book useful. Python programming experience is a must, knowledge of data science will be helpful but not necessary.

What you will learn

  • Design algorithms for solving complex problems
  • Become familiar with neural networks and deep learning techniques
  • Explore existing data structures and algorithms found in Python libraries
  • Implement graph algorithms for fraud detection using network analysis
  • Delve into state-of-the-art algorithms for proficient Natural Language Processing illustrated with real-world examples
  • Create a recommendation engine that suggests relevant movies to subscribers
  • Grasp the concepts of sequential machine learning models and their foundational role in the development of cutting-edge LLMs
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Publication date : Sep 29, 2023
Length: 538 pages
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Language : English
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Table of Contents

21 Chapters
Section 1: Fundamentals and Core Algorithms Chevron down icon Chevron up icon
Overview of Algorithms Chevron down icon Chevron up icon
Data Structures Used in Algorithms Chevron down icon Chevron up icon
Sorting and Searching Algorithms Chevron down icon Chevron up icon
Designing Algorithms Chevron down icon Chevron up icon
Graph Algorithms Chevron down icon Chevron up icon
Section 2: Machine Learning Algorithms Chevron down icon Chevron up icon
Unsupervised Machine Learning Algorithms Chevron down icon Chevron up icon
Traditional Supervised Learning Algorithms Chevron down icon Chevron up icon
Neural Network Algorithms Chevron down icon Chevron up icon
Algorithms for Natural Language Processing Chevron down icon Chevron up icon
Understanding Sequential Models Chevron down icon Chevron up icon
Advanced Sequential Modeling Algorithms Chevron down icon Chevron up icon
Section 3: Advanced Topics Chevron down icon Chevron up icon
Recommendation Engines Chevron down icon Chevron up icon
Algorithmic Strategies for Data Handling Chevron down icon Chevron up icon
Cryptography Chevron down icon Chevron up icon
Large-Scale Algorithms Chevron down icon Chevron up icon
Practical Considerations Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

Customer reviews

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3 star 7.5%
2 star 3%
1 star 4.5%
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Sandra Patricia Bermudez Kasperczak Feb 22, 2024
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N/A Feb 21, 2024
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Timothy J. Spann Nov 09, 2023
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This is a great reference book to check whenever you are going to be using common algorithms including some directly linked to machine learning. Following through this entire book in order would be a great way to get started in Com Sci, Python and/or Data Science.This is a good book to have in your library if you are a Data Engineer, Data Scientist or Python Programmer.
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William Appelbe Oct 11, 2023
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This book is an invaluable encyclopedia of useful algorithms. How to apply them, pitfalls and best practices. I really like the focus of modern Machine Learning (ML) and AI, including large language models (used by ChatGPT). Far too often, as a contractor and trainer, I see people who do not understand algorithms and take a package (with its builtin algorithms) and blindly use it and get bad results. This is especially true in ML and AII really like the fact that the book uses Python and Google collab and all code is available for download. With collab, too, you don’t need to install anything (collab is a free Python/Jupyter notebook hosted in the Cloud) to run the examples.One warning is that this book is not a “quick read”, nor will it make you an instant algorithm guru. It assumes intermediate programming skill and intermediate knowledge of math (high school Y12). That is because the author deliberately explains how the algorithms work and why and how to tune, not just which algorithm to use.The good news is that you do not have to read the book “cover to cover” to use it. Each section of the book is largely self-contained and well-organized (like an encyclopedia). So if you are interested in Ml Language models or recommender engines you can just read those chapters/sections; and ship the first section on Sorting algorithms (although familarity with them is a skill every programmer should have, even if in most cases the sorting algorithms are already implemented in most frameworks).
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