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Hacking Discrete Math With Python 3

Hacking Discrete Math With Python 3
Author: Isabella Romeo
Publisher: Createspace Independent Publishing Platform
Total Pages: 216
Release: 2018-06-11
Genre:
ISBN: 9781720405979
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Elementary discrete math for undergraduate computer science or computer engineering students. Covers basic topics including mathematical logic, direct proof, proof by contradiction, proof by contraposition, counter-example, induction, structural induction, elementary number theory, division, sets, sequences, functions, cardinality, counting, recurrence, recursion, and graph theory. Examples are given in Python 3.

Scientific Computation

Scientific Computation
Author: Bruce Shapiro
Publisher:
Total Pages: 546
Release: 2018-08-20
Genre:
ISBN: 9781725894662
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This is a book about hacking, but not just any kind of hacking. It is about mathematical hacking. If you like math and want to use computers to solve math problems, this book is for you. Scientific Computation: Python 3 Hacking for Math Junkies gives an introduction to hacking in Python for students and mathematical scientists. No previous coding experience is needed. This new edition has been updated to cover Python version 3. Computational applications are selected from many mathematical sub-disciplines. Examples include random numbers, statistics, finding roots, interpolation, linear and logistic regression, numerical solution of initial value problems, discrete systems, fractals, principal component analysis, singular value decomposition, clustering, image analysis, and satellite orbits. Over 300 exercises and projects are included for students. All code examples in the book are available for download from a companion website. The book is available in both print and electronic versions.

Scientific Computation

Scientific Computation
Author: Bruce E. Shapiro
Publisher:
Total Pages: 458
Release: 2015-01-11
Genre: Computer programming
ISBN: 9780692366936
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"This book is designed to help math junkies -- anyone who likes math, studies math, or uses math in their daily life -- learn about computation. The emphasis is on algorithms. It is appropriate for students with no prior programming experience as well as professional scientists. Topics covered include Python expressions, statements, types, lists, arrays, functions, classes, plotting, list comprehension, recursion, linear systems, computational geometry, root finding, interpolation, polynomial least squares, discrete systems, differential equations, principal component analysis, fractals and chaos."--Cover.

The Discrete Math Workbook

The Discrete Math Workbook
Author: Sergei Kurgalin
Publisher: Springer Nature
Total Pages: 507
Release: 2020-08-12
Genre: Computers
ISBN: 3030422216
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This practically-focused study guide introduces the fundamentals of discrete mathematics through an extensive set of classroom-tested problems. Each chapter presents a concise introduction to the relevant theory, followed by a detailed account of common challenges and methods for overcoming these. The reader is then encouraged to practice solving such problems for themselves, by tackling a varied selection of questions and assignments of different levels of complexity. This updated second edition now covers the design and analysis of algorithms using Python, and features more than 50 new problems, complete with solutions. Topics and features: provides a substantial collection of problems and examples of varying levels of difficulty, suitable for both laboratory practical training and self-study; offers detailed solutions to each problem, applying commonly-used methods and computational schemes; introduces the fundamentals of mathematical logic, the theory of algorithms, Boolean algebra, graph theory, sets, relations, functions, and combinatorics; presents more advanced material on the design and analysis of algorithms, including Turing machines, asymptotic analysis, and parallel algorithms; includes reference lists of trigonometric and finite summation formulae in an appendix, together with basic rules for differential and integral calculus. This hands-on workbook is an invaluable resource for undergraduate students of computer science, informatics, and electronic engineering. Suitable for use in a one- or two-semester course on discrete mathematics, the text emphasizes the skills required to develop and implement an algorithm in a specific programming language.

Practical Discrete Mathematics

Practical Discrete Mathematics
Author: Ryan T. White
Publisher: Packt Publishing Ltd
Total Pages: 330
Release: 2021-02-22
Genre: Mathematics
ISBN: 1838983503
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A practical guide simplifying discrete math for curious minds and demonstrating its application in solving problems related to software development, computer algorithms, and data science Key FeaturesApply the math of countable objects to practical problems in computer scienceExplore modern Python libraries such as scikit-learn, NumPy, and SciPy for performing mathematicsLearn complex statistical and mathematical concepts with the help of hands-on examples and expert guidanceBook Description Discrete mathematics deals with studying countable, distinct elements, and its principles are widely used in building algorithms for computer science and data science. The knowledge of discrete math concepts will help you understand the algorithms, binary, and general mathematics that sit at the core of data-driven tasks. Practical Discrete Mathematics is a comprehensive introduction for those who are new to the mathematics of countable objects. This book will help you get up to speed with using discrete math principles to take your computer science skills to a more advanced level. As you learn the language of discrete mathematics, you'll also cover methods crucial to studying and describing computer science and machine learning objects and algorithms. The chapters that follow will guide you through how memory and CPUs work. In addition to this, you'll understand how to analyze data for useful patterns, before finally exploring how to apply math concepts in network routing, web searching, and data science. By the end of this book, you'll have a deeper understanding of discrete math and its applications in computer science, and be ready to work on real-world algorithm development and machine learning. What you will learnUnderstand the terminology and methods in discrete math and their usage in algorithms and data problemsUse Boolean algebra in formal logic and elementary control structuresImplement combinatorics to measure computational complexity and manage memory allocationUse random variables, calculate descriptive statistics, and find average-case computational complexitySolve graph problems involved in routing, pathfinding, and graph searches, such as depth-first searchPerform ML tasks such as data visualization, regression, and dimensionality reductionWho this book is for This book is for computer scientists looking to expand their knowledge of discrete math, the core topic of their field. University students looking to get hands-on with computer science, mathematics, statistics, engineering, or related disciplines will also find this book useful. Basic Python programming skills and knowledge of elementary real-number algebra are required to get started with this book.

Hacking Math Class with Python

Hacking Math Class with Python
Author: Peter A. Farrell
Publisher: Createspace Independent Publishing Platform
Total Pages: 144
Release: 2015-02-26
Genre:
ISBN: 9781508656944
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A new kind of math book! Explore math topics from arithmetic to calculus by creating your own graphing and solving tools using Python. Create 2D and 3D graphics, harmonograph and spirograph designs, and fractals in this interactive and visual exploration of mathematics. "A great resource to play with Math and Python via the turtle module, solving equations numerically and 3D graphics via Pi3D." - Amit Saha, author of Doing Math With Python Imagine learning math and Python programming at the same time! You'll learn to use loops, variables, functions, conditionals and lists and apply them to all your math problems. No previous computer experience is required.

Hacking Python 3

Hacking Python 3
Author: Sanjib Sinha
Publisher:
Total Pages: 134
Release: 2017-03-11
Genre:
ISBN: 9781520813318
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Have you seen the film "The Matrix Reloaded"? Well, if you had seen you would have probably recalled the scene where the character Trinity was seen using NMAP to hack the system of a power plant. This book is all about Scanning, Networking and Information Gathering with the help of Python programming language and by the way teaches you major steps of Ethical Hacking.Contents:# Epilogue# PART ONE: LEGAL SIDE, CYBER CRIME AND NETWORKING# Chapter 1 - Legal Side of Hacking# Chapter 2 - Examples of Crime## 2.1 - Black Money and Bitcoin## 2.2 The Great Cyber Robberies ## 2.3 - Biggest Data Heist## 2.4 - Internet: Battleground for Women# Chapter 3 - Hacking and Networking## 3.1 - What Does Network Mean?#PART TWO: PYTHON AND HACKING# Chapter 4 - Object in Python# Chapter 5 - Conditionals# Chapter 6 - Loops## 6.1 - While Loops## 6.2 - For Loops# Chapter 7 - Regular Expressions ## 7.1 - Using 're' Module## 7.2 - Reusing With Regular Expressions## 7.3 - Search With Regular Expressions# Chapter 8. - Exceptions, Errors# Chapter 9 - Functions## 9.1 - Return Values## 9.2 - Generate Functions## 9.3 - Lists of Arguments## 9.4 - Named Arguments# Chapter 10 - Classes## 10.1 - Object Oriented Methodology## 10.2 - Classes and Objects## 10.3 - Write a Game "Good VS Bad"## 10.4 - Primary Class and Object## 10.5 - Accessing Object Data## 10.6 - Polymorphism## 10.7 - Using Generators## 10.8 -Decorator# Chapter 11 - File Input, Output# Chapter 12 - Containers## 12.1 - Tuple and List Object## 12.2 - Dictionary Object# Chapter 13 - Module# Chapter 14 - Debugging, UnitTestChapter 15 - Socket and Networking# Chapter 16 - Importing Nmap Module# Chapter 17 - Nmap Network Scanner#PART THREE: PYTHON AND SECURITY ANALYSIS, RECONNAISSANCE SCANNER# Chapter 18 - TLD Scanner# Chapter 19 - Get IP Address# Chapter 20 - Whois Search# Chapter 21 - NMAP Port Scan# Chapter 22 - Robots Exclusion# Prologue

Discrete Mathematical Algorithm, and Data Structures

Discrete Mathematical Algorithm, and Data Structures
Author: Sanjib Sinha
Publisher:
Total Pages: 650
Release: 2020-05-28
Genre:
ISBN:
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Readers will learn discrete mathematical abstracts as well as its implementation in algorithm and data structures shown in various programming languages, such as C, C++, PHP, Java, C#, Python and Dart. This book combines two major components of Mathematics and Computer Science under one roof. Without the core conceptions and tools derived from discrete mathematics, one cannot understand the abstract or the general idea involving algorithm and data structures in Computer Science. The objects of data structures are basically objects of discrete mathematics. This book tries to bridge the gap between two major components of Mathematics and Computer Science.In any computer science course, studying discrete mathematics is essential, although they are taught separately, except in a few cases. Yet, a comprehensive book, combining these two major components, is hard to find out; not only that, it is almost impossible to understand one without the help of other.Hope, this book will fill the gap. Readers will learn discrete mathematical abstracts as well as its implementation in algorithm and data structures shown in various programming language, such as C++, Java, C#, Python and Dart.1. Introduction to the Discourse Is Discrete Mathematics enough to study Computer Science? A short Introduction to Discrete Mathematics What is Discrete Mathematics What is the relationship between Discrete Mathematics and Computer Science Introducing necessary conceptions 2. Introduction to Programming Language and Boolean Algebra Logic, Mathematics, and Programming Language Introduction to Boolean Algebra 3. De Morgan's Laws on Boolean Algebra, Logical Expression, and Algorithm Logical Expression Short Circuit Evaluation Syntax, Semantics and Conditional Execution Why we need Control Constructs Discrete Mathematical Notations and Algorithm 4. Data Structures in different Programming languages Mean, Median and Mode Array, the First Step to Data Structure Let us understand some Array features Set Theory, Probability and Array Skewed Mean, Maximized Median Complex Array Algorithm 5. Data Structures: Abstractions and Implementation How objects work with each other More Algorithm and Time Complexity Introducing Data Structures How Calculus and Linear Algebra are Related to this Discourse 6. Data Structures in Detail Frequently Asked Questions about Data Structures Abstract Data Type (ADT) Linear Data Structures Modeling of a Structure ArrayList to overcome limitations of Array ArrayList or LinkedList, which is faster? Collection Framework in programming languages Stack and Queue in Java Deque, a high-performance Abstract Data Type 7. Algorithm, Data Structure, Collection Framework and Standard Template Library (STL) Introducing Algorithm Library Different types of Algorithms Binary Tree and Data Structure Collection Framework in Java Discrete Mathematical Abstractions and Implementation through Java Collection Comparator, Comparable and Iterator Standard Template Library in C++ 8. Time Complexity Order of n, or O(n) Big O Notation 9. Set, Symmetric Difference and Propositional Logic Why Set is important in Data Structures How Symmetric Difference and Propositional Logic combine 10. Combinatorics and Counting, Permutation and Combinations Permutation and Combination What Next

Python for Scientists

Python for Scientists
Author: John M. Stewart
Publisher: Cambridge University Press
Total Pages: 272
Release: 2017-07-20
Genre: Computers
ISBN: 1316641236
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Scientific Python is taught from scratch in this book via copious, downloadable, useful and adaptable code snippets. Everything the working scientist needs to know is covered, quickly providing researchers and research students with the skills to start using Python effectively.