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Practical Discrete Mathematics: Discover math principles that fuel algorithms for computer science and machine learning with Python
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Practical Discrete Mathematics: Discover math principles that fuel algorithms for computer science and machine learning with Python
A comprehensive introduction for those who are new to the mathematics of countable objects.
Practical Discrete Mathematics: Discover math principles that fuel algorithms for computer science and machine learning with Python
Item #: 40484439

Practical Discrete Mathematics: Discover math principles that fuel algorithms for computer science and machine learning with Python

88% of respondents would recommend this to a friend

Item #: 40484439

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A comprehensive introduction for those who are new to the mathematics of countable objects.
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What Stands Out

Algorithm Focus
Dive into essential math principles that directly support algorithm design, proving crucial for applications in computer science and machine learning.
Python Integration
Learn through practical examples using Python, enhancing understanding and skills in both programming and mathematical concepts simultaneously.
Comprehensive Insights
Offers a thorough exploration of discrete mathematics, addressing common challenges in computing and paving the way for innovation in technology.

Product Details

Explore practical discrete mathematics for algorithms and machine learning with Python at Ubuy Réunion. Get insights into math principles for computer science and enjoy a global shopping experience.
  • A practical guide simplifying discrete math for computer science and data science applications
  • Explore modern Python libraries like scikit-learn, NumPy, and SciPy for performing mathematics
  • Learn statistical and mathematical concepts through hands-on examples and expert guidance
  • Understand the terminology and methods in discrete math and their usage in algorithms and data problems
  • This book is for computer scientists and university students with basic Python programming skills
Publisher Packt Publishing
Publication date February 22, 2021
Language English
Print length 330 pages
ISBN-10 1838983147
ISBN-13 978-1838983147
Item Weight 1.36 pounds (620 grams)
Dimensions 7.5 x 0.75 x 9.25 inches (19.1 x 1.9 x 23.5 cm)
Part of series Communications in Computer and Information Science

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Who Should Buy?

Suitable For
  • Aspiring Programmers

    Those new to programming will benefit from practical applications of discrete math in real-world algorithms using Python.

  • Computer Science Students

    Students studying computer science will find valuable insights into the mathematical principles underlying key algorithms.

  • Data Scientists

    Data professionals looking to enhance their understanding of algorithms in machine learning can leverage discrete math effectively.

Not Suitable For
  • Complete Beginners

    Individuals with no background in mathematics may struggle with the concepts without foundational knowledge.

Product Description

Practical Discrete Mathematics: Discover math principles that fuel algorithms for computer science and machine learning with Python

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Customer Questions & Answers

  • Question: What topics are covered in Practical Discrete Mathematics?

    Answer: Practical Discrete Mathematics covers essential topics such as combinatorics, graph theory, algorithms, and probability. These principles are crucial for understanding the underpinnings of computer science and machine learning techniques with Python. Studying these topics helps illustrate how abstract mathematical concepts translate into practical applications, like optimizing algorithms and improving data analysis. For instance, graph theory can be used to enhance network algorithms or recommendation systems in machine learning.
  • Question: Who is the intended audience for this book?

    Answer: This book is designed for students, professionals, and enthusiasts in computer science or data science fields who want to deepen their understanding of discrete mathematics. It is particularly beneficial for those focusing on algorithm design or machine learning. Readers can expect to gain a solid mathematical foundation that will aid in developing efficient Python programs and contribute to their skill set in tech-driven environments. For example, a software engineer may utilize concepts from this book to streamline data processing tasks.
  • Question: How can Practical Discrete Mathematics be beneficial for machine learning?

    Answer: The book provides a thorough understanding of mathematical principles that are fundamental in machine learning, such as optimization techniques and probability theory. By mastering these concepts, readers can implement more robust machine learning algorithms and improve model performance. For instance, grasping the concept of probability is vital when working with data analysis and predictive modeling, ensuring more accurate outputs and better decision-making processes in real-world applications.
  • Question: What programming language is used in the book?

    Answer: Practical Discrete Mathematics primarily utilizes Python for its examples and exercises. Python's simplicity and versatility make it a popular choice in the fields of computer science and machine learning, allowing readers to quickly implement mathematical concepts into code. By learning through Python, readers can directly see how mathematics impacts algorithm performance, aiding in the transition from theory to practical coding skills. For instance, solving graph-related problems in Python can quickly illustrate the effectiveness of an algorithm.
  • Question: Are there exercises included in the book?

    Answer: Yes, the book includes numerous exercises designed to reinforce the concepts explained in each chapter. These exercises range from theoretical questions to practical programming tasks, providing a comprehensive learning experience. Engaging with these exercises will help solidify understanding and application of discrete mathematics principles. For instance, readers might be tasked with creating a Python script that implements a specific algorithm, enhancing both their coding and mathematical reasoning skills.
  • Question: Can this book help in preparing for technical interviews?

    Answer: Absolutely! Practical Discrete Mathematics equips readers with foundational knowledge of algorithms and data structures, which are crucial for technical interviews in tech companies. Mastering these topics can significantly enhance problem-solving skills and analytical thinking, making candidates more competitive. Moreover, understanding discrete mathematics can help in tackling coding challenges related to efficiency and optimization typically found in interviews, such as those requiring algorithmic design or complexity analysis.
  • Question: Is prior knowledge of mathematics required to read this book?

    Answer: While some familiarity with basic mathematics is helpful, the book is structured to guide readers from fundamental concepts to more advanced topics in discrete mathematics. The authors strive to explain complex ideas in a straightforward manner, making it accessible to those without an extensive math background. This ensures that even beginners can gradually build their knowledge and confidence. Readers can start with foundational topics, such as sets and functions, before moving on to more complex materials.
  • Question: How does the book address the application of mathematics in algorithms?

    Answer: The book integrates mathematical theories directly with algorithmic applications, illustrating how discrete mathematics serves as a backbone for effective algorithm design. Each theoretical concept is paired with real-world examples and case studies that demonstrate its relevance. This hands-on approach helps readers understand the 'why' behind algorithm choices. For instance, exploring the mathematical principles behind sorting algorithms can clarify their efficiency and use in diverse programming scenarios.
  • Question: Is there a focus on real-world applications in this book?

    Answer: Yes, Practical Discrete Mathematics emphasizes real-world applications throughout its chapters. The authors provide examples and case studies that highlight how discrete mathematical principles are applied in various fields, particularly in computer science and machine learning. This practical focus allows readers to see the relevance of their studies in real tech situations, such as data analysis and software development. For example, understanding combinatorial optimization can lead to better resource management in tech projects.
  • Question: Where can I buy Practical Discrete Mathematics in Réunion?

    Answer: You can purchase Practical Discrete Mathematics from Ubuy, a reliable online marketplace offering a wide selection of books and educational materials. Ubuy provides customers in Réunion access to this title, ensuring a convenient shopping experience. By choosing Ubuy, you benefit from their commitment to delivering quality products alongside comprehensive customer support, helping you obtain the resources you need for your studies.

Linear Programming Editorial Review

Practical Discrete Mathematics is a great book for both beginners and experts in the field of computer science and machine learning. The book provides excellent examples in Python, making it easier to understand the concepts. Readers who have prior knowledge in Boolean algebra or logic will find the book a breeze to read. The codes provided in the book work perfectly well. The book is clearly written and can provide readers with a different perspective on what they already know.

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Pros

  • Provides a great introduction to discrete mathematics
  • Excellent examples in Python
  • Clear and easy to understand
  • Provides a different perspective on concepts already known

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Features & Benefits

  • Discover math principles that fuel algorithms for computer science and machine learning
  • Apply the math of countable objects to practical problems in computer science
  • Explore modern Python libraries for performing mathematics
  • Learn complex statistical and mathematical concepts with hands-on examples
  • Understand the terminology and methods in discrete math and their usage in algorithms
  • Perform ML tasks such as data visualization, regression, and dimensionality reduction

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