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Home> Programming> Data Structures and Algorithms> 50 Algorithms Every Programmer Should Know
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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Full star iconFull star iconFull star iconFull star iconHalf star icon4.5(68 Ratings)
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50 Algorithms Every Programmer Should Know

Fundamentals and Core Algorithms

This section introduces the core aspects of algorithms. We will explore what an algorithm is and how to design one. 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 1,Overview of Algorithms
  • Chapter 2,Data Structures Used in Algorithms
  • Chapter 3,Sorting and Searching Algorithms
  • Chapter 4,Designing Algorithms
  • Chapter 5,Graph Algorithms
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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

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date :Sep 29, 2023
Length:538 pages
Edition :2nd
Language :English
ISBN-13 :9781803246475
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Publication date :Sep 29, 2023
Length:538 pages
Edition :2nd
Language :English
ISBN-13 :9781803246475
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Table of Contents

21 Chapters
Section 1: Fundamentals and Core AlgorithmsChevron down iconChevron up icon
Section 1: Fundamentals and Core Algorithms
Overview of AlgorithmsChevron down iconChevron up icon
Overview of Algorithms
What is an algorithm?
Python packages
Algorithm design techniques
Performance analysis
Selecting an algorithm
Validating an algorithm
Summary
Data Structures Used in AlgorithmsChevron down iconChevron up icon
Data Structures Used in Algorithms
Exploring Python built-in data types
Exploring abstract data types
Summary
Sorting and Searching AlgorithmsChevron down iconChevron up icon
Sorting and Searching Algorithms
Introducing sorting algorithms
Introduction to searching algorithms
Practical applications
Summary
Designing AlgorithmsChevron down iconChevron up icon
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 AlgorithmsChevron down iconChevron up icon
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 AlgorithmsChevron down iconChevron up icon
Section 2: Machine Learning Algorithms
Unsupervised Machine Learning AlgorithmsChevron down iconChevron up icon
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 AlgorithmsChevron down iconChevron up icon
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
For regression algorithms, the winner is...
Practical example – how to predict the weather
Summary
Neural Network AlgorithmsChevron down iconChevron up icon
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 ProcessingChevron down iconChevron up icon
Algorithms for Natural Language Processing
Introducing NLP
Understanding NLP terminology
Cleaning data using Python
Understanding the Term Document Matrix
Introduction to word embedding
Implementing word embedding with Word2Vec
Case study: Restaurant review sentiment analysis
Applications of NLP
Summary
Understanding Sequential ModelsChevron down iconChevron up icon
Understanding Sequential Models
Understanding sequential data
Data representation for sequential models
Introducing RNNs
GRU
Introducing LSTM
Summary
Advanced Sequential Modeling AlgorithmsChevron down iconChevron up icon
Advanced Sequential Modeling Algorithms
The evolution of advanced sequential modeling techniques
Exploring autoencoders
Understanding the Seq2Seq model
Understanding the attention mechanism
Delving into self-attention
Transformers: the evolution in neural networks after self-attention
LLMs
Bottom of Form
Summary
Section 3: Advanced TopicsChevron down iconChevron up icon
Section 3: Advanced Topics
Recommendation EnginesChevron down iconChevron up icon
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 HandlingChevron down iconChevron up icon
Algorithmic Strategies for Data Handling
Introduction to data algorithms
Presenting the CAP theorem
Decoding data compression algorithms
Practical example: Data management in AWS: A focus on CAP theorem and compression algorithms
Summary
CryptographyChevron down iconChevron up icon
Cryptography
Introduction to cryptography
Understanding the types of cryptographic techniques
Example: security concerns when deploying a machine learning model
Summary
Large-Scale AlgorithmsChevron down iconChevron up icon
Large-Scale Algorithms
Introduction to large-scale algorithms
Characterizing performant infrastructure for large-scale algorithms
Strategizing multi-resource processing
Understanding theoretical limitations of parallel computing
How Apache Spark empowers large-scale algorithm processing
Using large-scale algorithms in cloud computing
Summary
Practical ConsiderationsChevron down iconChevron up icon
Practical Considerations
Challenges facing algorithmic solutions
Failure of Tay, the Twitter AI bot
The explainability of an algorithm
Understanding ethics and algorithms
Reducing bias in models
When to use algorithms
Summary
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IndexChevron down iconChevron up icon
Index

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Customer reviews

Top Reviews
Rating distribution
Full star iconFull star iconFull star iconFull star iconHalf star icon4.5
(68 Ratings)
5 star75%
4 star10.3%
3 star7.4%
2 star2.9%
1 star4.4%
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Sandra Patricia Bermudez KasperczakFeb 22, 2024
Full star iconFull star iconFull star iconFull star iconFull star icon5
Feefo Verified reviewFeefo
N/AFeb 21, 2024
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N/AFeb 19, 2024
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Timothy J. SpannNov 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.
Amazon Verified reviewAmazon
William AppelbeOct 11, 2023
Full star iconFull star iconFull star iconFull star iconFull star icon5
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).
Amazon Verified reviewAmazon
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About the author

Profile icon Imran Ahmad
Imran Ahmad
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Imran Ahmad is the author of the “50 Algorithms every programmer should know”. He has been a part of cutting-edge research about algorithms and machine learning for many years. He completed his PhD in 2010, in which he proposed a new linear programming-based algorithm that can be used to optimally assign resources in a large-scale cloud computing environment. In 2017, Imran developed a real-time analytics framework named StreamSensing. He has since authored multiple research papers that use StreamSensing to process multimedia data for various machine learning algorithms. Imran is currently working at Advanced Analytics Solution Center (A2SC) at the Canadian Federal Government as a data scientist. He is using machine learning algorithms for critical use cases. Imran is a visiting professor at Carleton University, Ottawa. He has also been teaching for Google and Learning Tree for the last few years.
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