Section 1: Fundamentals and Core Algorithms
Chapter 1, Overview of Algorithms, provides insight into the fundamentals of algorithms. It starts with the basic concepts of algorithms, how people started using algorithms to formulate problems, and the limitations of different algorithms. As Python is used in this book to write the algorithms, how to set up a Python environment to run the examples is explained. We will then look at how an algorithm’s performance can be quantified and compared against other algorithms.
Chapter 2, Data Structures Used in Algorithms, discusses data structures in the context of algorithms. As we are using Python in this book, this chapter focuses on Python data structures, but the concepts presented can be used in other languages such as Java and C++. This chapter will show you how Python handles complex data structures and which structures should be used for certain types of data.
Chapter 3, Sorting and Searching Algorithms, starts by presenting different types of sorting algorithms and various approaches for designing them. Then, following practical examples, searching algorithms are also discussed.
Chapter 4, Designing Algorithms, covers the choices available to us when designing algorithms, discussing the importance of characterizing the problem that we are trying to solve. Next, it uses the famous Traveling Salesperson Problem (TSP) as a use case and applies the design techniques that we will be presenting. It also introduces linear programming and discusses its applications.
Chapter 5, Graph Algorithms, covers the ways we can capture graphs to represent data structures. It covers some foundational theories, techniques, and methods relating to graph algorithms, such as network theory analysis and graph traversals. We will investigate a case study using graph algorithms to delve into fraud analytics.
Section 2: Machine Learning Algorithms
Chapter 6, Unsupervised Machine Learning Algorithms, explains how unsupervised learning can be applied to real-world problems. We will learn about its basic algorithms and methodologies, such as clustering algorithms, dimensionality reduction, and association rule mining.
Chapter 7, Traditional Supervised Learning Algorithms, delves into the essentials of supervised machine learning, featuring classifiers and regressors. We will explore their capabilities using real-world problems as case studies. Six distinct classification algorithms are presented, followed by three regression techniques. Lastly, we’ll compare their results to encapsulate the key takeaways from this discussion.
Chapter 8, Neural Network Algorithms, introduces the main concepts and components of a typical neural network. It then presents the various types of neural networks and the activation functions used in them. The backpropagation algorithm is discussed in detail, which is the most widely used algorithm for training a neural network. Finally, we will learn how to use deep learning to flag fraudulent documents by way of a real-world example application.
Chapter 9, Algorithms for Natural Language Processing, introduces algorithms for natural language processing (NLP). It introduces the fundamentals of NLP and how to prepare data for NLP tasks. After that, it explains the concepts of vectorizing textual data and word embeddings. Finally, we present a detailed use case.
Chapter 10, Understanding Sequential Models, looks into training neural networks for sequential data. It covers the core principles of sequential models, providing an introductory overview of their techniques and methodologies. It will then consider how deep learning can improve NLP techniques.
Chapter 11, Advanced Sequential Modeling Algorithms, considers the limitations of sequential models and how sequential modeling has evolved to overcome these limitations. It delves deeper into the advanced aspects of sequential models to understand the creation of complex configurations. It starts by breaking down key elements, such as autoencoders and Sequence-to-Sequence (Seq2Seq) models. Next, it looks into attention mechanism and transformers, which are pivotal in the development of Large Language Models (LLMs), which we will then study.
Section 3: Advanced Topics
Chapter 12, Recommendation Engines, covers the main types of recommendation engines and the inner workings of each. These systems are adept at suggesting tailored items or products to users, but they’re not without their challenges. We’ll discuss both their strengths and the limitations they present. Finally, we will learn how to use recommendation engines to solve a real-world problem.
Chapter 13, Algorithmic Strategies for Data Handling, introduces data algorithms and the basic concepts behind the classification of data. We will look at the data storage and data compression algorithms used to efficiently manage data, helping us to understand the trade-offs involved in designing and implementing data-centric algorithms.
Chapter 14, Cryptography, introduces you to algorithms related to cryptography. We will start by presenting the background of cryptography before discussing symmetric encryption algorithms. We will learn about the Message-Digest 5 (MD5) algorithm and the Secure Hash Algorithm (SHA), presenting the limitations and weaknesses of each. Then, we will discuss asymmetric encryption algorithms and how they are used to create digital certificates. Finally, we will present a practical example that summarizes all of these techniques.
Chapter 15, Large-Scale Algorithms, starts by introducing large-scale algorithms and the efficient infrastructure required to support them. We will explore various strategies for managing multi-resource processing. We will examine the limitations of parallel processing, as outlined by Amdahl’s law, and investigate the use of Graphics Processing Units (GPUs). Upon completing this chapter, you will have gained a solid foundation in the fundamental strategies essential for designing large-scale algorithms.
Chapter 16, Practical Considerations, presents the issues around the explainability of an algorithm, which is the degree to which the internal mechanics of an algorithm can be explained in understandable terms. Then, we will present the ethics of using an algorithm and the possibility of creating biases when implementing them. Next, the techniques for handling NP-hard problems will be discussed. Finally, we will investigate factors that should be considered before choosing an algorithm.
Download the example code files
The code bundle for the book is also hosted on GitHub at https://github.com/cloudanum/50Algorithms. We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out! You can also find the same code bundle on Google Drive at http://code.50algo.com.
Download the color images
We also provide a PDF file that has color images of the screenshots and diagrams used in this book. You can download it here: https://packt.link/UBw6g.
Conventions used
There are a number of text conventions used throughout this book.
Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: “Let’s try to create a simple graph using the networtx package in Python.”
Bold: Indicates a new term, an important word, or words that you see onscreen. For example, new terms appear in text like this: “Python is also one of the languages that you can use in various cloud computing infrastructures, such as Amazon Web Services (AWS) and Google Cloud Platform (GCP).”
Warnings or important notes appear like this.
Tips and tricks appear like this
