Book description
Delve into the realm of generative AI and large language models (LLMs) while exploring modern deep learning techniques, including LSTMs, GRUs, RNNs with new chapters included in this 50% new edition overhaul Purchase of the print or Kindle book includes a free eBook in PDF format.
Key Features
 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
Book Description
The ability to use algorithms to solve realworld problems is a musthave 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 computeintensive tasks.
By the end of this programming book, you'll have become adept at solving realworld computational problems by using a wide range of algorithms.
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 stateoftheart algorithms for proficient Natural Language Processing illustrated with realworld 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 cuttingedge LLMs
Who this book is for
This computer science book is for programmers or developers who want to understand the use of algorithms for problemsolving and writing efficient code. Whether you are a beginner looking to learn the most used algorithms concisely or an experienced programmer looking to explore cuttingedge 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.
Table of contents
 Preface
 Section 1: Fundamentals and Core Algorithms
 Overview of Algorithms
 Data Structures Used in Algorithms
 Sorting and Searching Algorithms

Designing Algorithms
 Introducing the basic concepts of designing an algorithm
 Understanding algorithmic strategies
 A practical application – solving the TSP
 Presenting the PageRank algorithm
 Understanding linear programming
 Summary

Graph Algorithms
 Understanding graphs: a brief introduction
 Graph theory and network analysis
 Representations of graphs
 Graph mechanics and types
 Introducing network analysis theory
 Understanding graph traversals
 Case study: fraud detection using SNA
 Summary
 Section 2: Machine Learning Algorithms

Unsupervised Machine Learning Algorithms
 Introducing unsupervised learning
 Understanding clustering algorithms
 Steps of hierarchical clustering
 Coding a hierarchical clustering algorithm
 Understanding DBSCAN
 Creating clusters using DBSCAN in Python
 Evaluating the clusters
 Dimensionality reduction
 Association rules mining
 Summary

Traditional Supervised Learning Algorithms
 Understanding supervised machine learning
 Formulating supervised machine learning problems
 Understanding classification algorithms
 Decision tree classification algorithm
 Understanding the ensemble methods
 Logistic regression
 The SVM algorithm
 Bayes’ theorem
 For classification algorithms, the winner is...

Linear regression
 Simple linear regression
 Evaluating the regressors
 Multiple regression
 Using the linear regression algorithm for the regressors challenge
 When is linear regression used?
 The weaknesses of linear regression
 The regression tree algorithm
 Using the regression tree algorithm for the regressors challenge
 The gradient boost regression algorithm
 Using the gradient boost regression algorithm for the regressors challenge
 For regression algorithms, the winner is...
 Practical example – how to predict the weather
 Summary

Neural Network Algorithms
 The evolution of neural networks
 Understanding neural networks
 Training a neural network
 Understanding the anatomy of a neural network
 Defining gradient descent
 Activation functions
 Tools and frameworks
 Choosing a sequential or functional model
 Understanding the types of neural networks
 Using transfer learning
 Case study – using deep learning for fraud detection
 Summary
 Algorithms for Natural Language Processing
 Understanding Sequential Models
 Advanced Sequential Modeling Algorithms
 Section 3: Advanced Topics

Recommendation Engines
 Introducing recommendation systems
 Types of recommendation engines
 Understanding the limitations of recommendation systems
 Areas of practical applications
 Practical example – creating a recommendation engine
 Summary
 Algorithmic Strategies for Data Handling

Cryptography
 Introduction to cryptography
 Understanding the types of cryptographic techniques
 Example: security concerns when deploying a machine learning model
 Summary

LargeScale Algorithms
 Introduction to largescale algorithms
 Characterizing performant infrastructure for largescale algorithms
 Strategizing multiresource processing
 Understanding theoretical limitations of parallel computing
 How Apache Spark empowers largescale algorithm processing
 Using largescale algorithms in cloud computing
 Summary
 Practical Considerations
 Other Books You May Enjoy
 Index
Product information
 Title: 50 Algorithms Every Programmer Should Know  Second Edition
 Author(s):
 Release date: September 2023
 Publisher(s): Packt Publishing
 ISBN: 9781803247762
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