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50 Algorithms Every Programmer Should Know

50 Algorithms Every Programmer Should Know - Second Edition

By : Imran Ahmad
4.5 (64)
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50 Algorithms Every Programmer Should Know

50 Algorithms Every Programmer Should Know

4.5 (64)
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)
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Lock 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

Coding a hierarchical clustering algorithm

Let’s learn how we can code a hierarchical algorithm in Python:

  1. We will first import AgglomerativeClustering from the sklearn.cluster library, along with the pandas and numpy packages:
    from sklearn.cluster import AgglomerativeClustering
    import pandas as pd
    import numpy as np
    
  2. Then we will create 20 data points in a two-dimensional problem space:
    dataset = pd.DataFrame({
        'x': [11, 11, 20, 12, 16, 33, 24, 14, 45, 52, 51, 52, 55, 53, 55, 61, 62, 70, 72, 10],
        'y': [39, 36, 30, 52, 53, 46, 55, 59, 12, 15, 16, 18, 11, 23, 14, 8, 18, 7, 24, 70]
    })
    
  3. Then we create the hierarchical cluster by specifying the hyperparameters. Note that a hyperparameter refers to a configuration parameter of a machine learning model that is set before the training process and influences the model’s behavior and performance. We use the fit_predict function to actually process...
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50 Algorithms Every Programmer Should Know
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