Search results for "Handbook of Computer Programming with Python"
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R 434.00
R 916.00
Python Programming for Mathematics
Python Programming for Mathematics focuses on the practical use of the Python language in a range of different areas of mathematics. Through fifty-five exercises of increasing difficulty, the book provides an expansive overview of the power of using programming to solve complex mathematical problems.This book is intended for undergraduate and graduate students who already have learned the basics of Python programming and would like to learn how to apply that programming skill in mathematics.FeaturesInnovative style that teaches programming skills via mathematical exercises.Ideal as a main textbook for Python for Mathematics courses, or as a supplementary resource for Numerical Analysis and Scientific Computing courses.
R 8,098.04
Hands-On Image Processing with Python
Explore the mathematical computations and algorithms for image processing using popular Python tools and frameworks.Key FeaturesPractical coverage of every image processing task with popular Python librariesIncludes topics such as pseudo-coloring, noise smoothing, computing image descriptorsCovers popular machine learning and deep learning techniques for complex image processing tasksBook DescriptionImage processing plays an important role in our daily lives with various applications such as in social media (face detection), medical imaging (X-ray, CT-scan), security (fingerprint recognition) to robotics & space. This book will touch the core of image processing, from concepts to code using Python.The book will start from the classical image processing techniques and explore the evolution of image processing algorithms up to the recent advances in image processing or computer vision with deep learning. We will learn how to use image processing libraries such as PIL, scikit-mage, and scipy ndimage in Python. This book will enable us to write code snippets in Python 3 and quickly implement complex image processing algorithms such as image enhancement, filtering, segmentation, object detection, and classification. We will be able to use machine learning models using the scikit-learn library and later explore deep CNN, such as VGG-19 with Keras, and we will also use an end-to-end deep learning model called YOLO for object detection. We will also cover a few advanced problems, such as image inpainting, gradient blending, variational denoising, seam carving, quilting, and morphing.By the end of this book, we will have learned to implement various algorithms for efficient image processing.What you will learnPerform basic data pre-processing tasks such as image denoising and spatial filtering in PythonImplement Fast Fourier Transform (FFT) and Frequency domain filters (e.g., Weiner) in PythonDo morphological image processing and segment images with different algorithmsLearn techniques to extract features from images and match imagesWrite Python code to implement supervised / unsupervised machine learning algorithms for image processingUse deep learning models for image classification, segmentation, object detection and style transferWho this book is forThis book is for Computer Vision Engineers, and machine learning developers who are good with Python programming and want to explore details and complexities of image processing. No prior knowledge of the image processing techniques is expected.
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CNC Machining Handbook: Building, Programming, and Implementation
Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product.A Practical Guide to CNC MachiningGet a thorough explanation of the entire CNC process from start to finish, including the various machines and their uses and the necessary software and tools. CNC Machining Handbook describes the steps involved in building a CNC machine to custom specifications and successfully implementing it in a real-world application. Helpful photos and illustrations are featured throughout. Whether you're a student, hobbyist, or business owner looking to move from a manual manufacturing process to the accuracy and repeatability of what CNC has to offer, you'll benefit from the in-depth information in this comprehensive resource.CNC Machining Handbook covers:Common types of home and shop-based CNC-controlled applicationsLinear motion guide systemsTransmission systemsStepper and servo motorsController hardwareCartesian coordinate systemCAD (computer-aided drafting) and CAM (computer-aided manufacturing) softwareOverview of G code languageReady-made CNC systems
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Programming Bitcoin
Dive into Bitcoin technology with this hands-on guide from one of the leading teachers on Bitcoin and Bitcoin programming. Author Jimmy Song shows Python programmers and developers how to program a Bitcoin library from scratch. You’ll learn how to work with the basics, including the math, blocks, network, and transactions behind this popular cryptocurrency and its blockchain payment system. By the end of the book, you'll understand how this cryptocurrency works under the hood by coding all the components necessary for a Bitcoin library. Learn how to create transactions, get the data you need from peers, and send transactions over the network. Whether you’re exploring Bitcoin applications for your company or considering a new career path, this practical book will get you started. Parse, validate, and create bitcoin transactions Learn how to use the Bitcoin Script smart contract language Work with exercises in each chapter to build a Bitcoin library from scratch Understand how to secure a blockchain by verifying proof of work Program Bitcoin using Python 3 and libraries including pycoin Understand how simplified payment verification and light wallets work Work with public-key cryptography and cryptographic primitives
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Practical Numerical and Scientific Computing with MATLAB® and Python
Practical Numerical and Scientific Computing with MATLAB® and Python concentrates on the practical aspects of numerical analysis and linear and non-linear programming. It discusses the methods for solving different types of mathematical problems using MATLAB and Python. Although the book focuses on the approximation problem rather than on error analysis of mathematical problems, it provides practical ways to calculate errors.The book is divided into three parts, covering topics in numerical linear algebra, methods of interpolation, numerical differentiation and integration, solutions of differential equations, linear and non-linear programming problems, and optimal control problems.This book has the following advantages:It adopts the programming languages, MATLAB and Python, which are widely used among academics, scientists, and engineers, for ease of use and contain many libraries covering many scientific and engineering fields.It contains topics that are rarely found in other numerical analysis books, such as ill-conditioned linear systems and methods of regularization to stabilize their solutions, nonstandard finite differences methods for solutions of ordinary differential equations, and the computations of the optimal controls. It provides a practical explanation of how to apply these topics using MATLAB and Python.It discusses software libraries to solve mathematical problems, such as software Gekko, pulp, and pyomo. These libraries use Python for solutions to differential equations and static and dynamic optimization problems.Most programs in the book can be applied in versions prior to MATLAB 2017b and Python 3.7.4 without the need to modify these programs.This book is aimed at newcomers and middle-level students, as well as members of the scientific community who are interested in solving math problems using MATLAB or Python.
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Practical Graph Intelligence 1
Practical Graph Intelligence 1 is positioned at the intersection of graph theory, network science and applied computing, offering a structured pathway for understanding and implementing graph-based solutions. This book systematically develops core concepts in graph algorithms and network analysis, while emphasizing practical implementation using Python. It explores fundamental structures, traversal techniques, optimization strategies and real-world network modeling, enabling readers to translate theory into scalable applications. Through clear explanations and hands-on examples, the book supports learners in building analytical skills required for domains such as artificial intelligence (AI), data science, cybersecurity and social network analysis. Designed for students, researchers and professionals, this book bridges the gap between mathematical foundations and computational practice, fostering the development of efficient and intelligent network-driven systems.
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Real World Instrumentation with Python
With this convenient, single-source reference, you'll learn how to develop instrumentation software with Python that lets you monitor or control devices in the physical world. Whether you need to acquire data from a hardware device or automate its functions, this book shows you how to build interfaces that include everything from software to wiring. You get step-by-step instructions, clear examples, and hands-on tips and hints for interfacing a PC to the real world, along with advice to help you avoid common pitfalls. Use the book's hardware survey to discover the interface type for your particular device, and then follow detailed examples to develop the interface with Python and C. Organized by interface type, data processing activities, and user interface implementations, this book is for anyone who works with instrumentation, robotics, data acquisition, or process control.* Understand how to define the scope of an application and determine the algorithms necessary -- and why it's important * Learn how to use industry-standard interfaces such as RS-232, RS-485, and GPIB * Create low-level extension modules in C to interface Python with a variety of hardware and test instruments * Explore the console, curses, TkInter, and wxPython for graphical and text-based user interfaces * Use open source software tools and libraries to reduce cost and avoid implementing functionality from scratch
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Bayesian Programming
Probability as an Alternative to Boolean LogicWhile logic is the mathematical foundation of rational reasoning and the fundamental principle of computing, it is restricted to problems where information is both complete and certain. However, many real-world problems, from financial investments to email filtering, are incomplete or uncertain in nature. Probability theory and Bayesian computing together provide an alternative framework to deal with incomplete and uncertain data. Decision-Making Tools and Methods for Incomplete and Uncertain DataEmphasizing probability as an alternative to Boolean logic, Bayesian Programming covers new methods to build probabilistic programs for real-world applications. Written by the team who designed and implemented an efficient probabilistic inference engine to interpret Bayesian programs, the book offers many Python examples that are also available on a supplementary website together with an interpreter that allows readers to experiment with this new approach to programming.Principles and Modeling Only requiring a basic foundation in mathematics, the first two parts of the book present a new methodology for building subjective probabilistic models. The authors introduce the principles of Bayesian programming and discuss good practices for probabilistic modeling. Numerous simple examples highlight the application of Bayesian modeling in different fields.Formalism and AlgorithmsThe third part synthesizes existing work on Bayesian inference algorithms since an efficient Bayesian inference engine is needed to automate the probabilistic calculus in Bayesian programs. Many bibliographic references are included for readers who would like more details on the formalism of Bayesian programming, the main probabilistic models, general purpose algorithms for Bayesian inference, and learning problems.FAQsAlong with a glossary, the fourth part contains answers to frequently asked questions. The authors compare Bayesian programming and possibility theories, discuss the computational complexity of Bayesian inference, cover the irreducibility of incompleteness, and address the subjectivist versus objectivist epistemology of probability. The First Steps toward a Bayesian ComputerA new modeling methodology, new inference algorithms, new programming languages, and new hardware are all needed to create a complete Bayesian computing framework. Focusing on the methodology and algorithms, this book describes the first steps toward reaching that goal. It encourages readers to explore emerging areas, such as bio-inspired computing, and develop new programming languages and hardware architectures.
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Cracking Codes With Python
Cracking Secret Codes with Python is a hands-on introduction to Python that teaches readers how to make and hack cipher programs, which are used to encrypt secret messages. It covers ciphers like the Caesar cipher, transposition cipher, and the RSA cipher, and teaches readers how to test and hack them. For every program, Sweigart provides the full source code and then walks readers through it, explaining how every line works. Along the way, readers will learn Python fundamentals - and by the book's end, they'll have a solid foundation in Python and some fun programs under their belt.
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Introduction to Digital Music with Python Programming
Introduction to Digital Music with Python Programming provides a foundation in music and code for the beginner. It shows how coding empowers new forms of creative expression while simplifying and automating many of the tedious aspects of production and composition.With the help of online, interactive examples, this book covers the fundamentals of rhythm, chord structure, and melodic composition alongside the basics of digital production. Each new concept is anchored in a real-world musical example that will have you making beats in a matter of minutes.Music is also a great way to learn core programming concepts such as loops, variables, lists, and functions, Introduction to Digital Music with Python Programming is designed for beginners of all backgrounds, including high school students, undergraduates, and aspiring professionals, and requires no previous experience with music or code.
R 6,866.42
Hands-On Object-Oriented Programming
Learn how to easily design and implement a software system using object-oriented programming. Through hands-on activities, this book equips you with the real-world practice needed to master OOP skills that can be applied universally across any programming language. This book is dedicated to bridging the gap between theoretical concepts and practical implementation in software development using OOP. Readers will first understand the importance of OOP in handling software complexities before learning how to model real-world problems through access control, extendible object design, and object relationships. Specifically, the author discusses how to use inheritance, association, and polymorphism concepts for implementing reusable and easy-to-use complex software. More importantly, this book covers design patterns and event-driven programming for learning how to model complex interactions among various objects of the software system to implement various use cases. Unlike existing books on OOP which teach syntax and semantics through standard examples, this book leverages an immersive learning experience through a variety of real-world use cases, applications, games, and system software. While written in C++, the skills can be applied to any OOP language of the reader's preference. The book's last chapters will quickly introduce Python and Solidity languages for learning advanced technologies. By the end, novice and seasoned developers will know how to implement practical and reusable software systems with confidence. What You Will Learn Apply OOP concepts through real-time use cases: games, applications software (online shopping, reservations, etc.), system software (protocols, simulators, etc.), and modeling and implementation using C+Design and implement reusable and extendible software with OOPUnderstand the importance of design patterns and learn how to use them for solving real-time issuesUse event-driven programming for implementing complete interactions of complex application software, system software, and simulatorsImplement OOP using C++ features and discover Python and Solidity constructs for OOP Who This Book Is For Beginners and intermediate software developers with basic programming knowledge to quickly learn OOP concepts with C++. Advanced users will find it useful for quickly exploring best-design principles, patterns, and event-driven programming for implementing complex software systems.
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R 550.00
Handbook of Mathematical Models with Python
Master the art of mathematical modeling through practical examples, use cases, and machine learning techniquesKey FeaturesGain a profound understanding of various mathematical models that can be integrated with machine learningLearn how to implement optimization algorithms to tune machine learning modelsBuild optimal solutions for practical use casesPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionMathematical modeling is the art of transforming a business problem into a well-defined mathematical formulation. Its emphasis on interpretability is particularly crucial when deploying a model to support high-stake decisions in sensitive sectors like pharmaceuticals and healthcare.Through this book, you’ll gain a firm grasp of the foundational mathematics underpinning various machine learning algorithms. Equipped with this knowledge, you can modify algorithms to suit your business problem. Starting with the basic theory and concepts of mathematical modeling, you’ll explore an array of mathematical tools that will empower you to extract insights and understand the data better, which in turn will aid in making optimal, data-driven decisions. The book allows you to explore mathematical optimization and its wide range of applications, and concludes by highlighting the synergetic value derived from blending mathematical models with machine learning.Ultimately, you’ll be able to apply everything you’ve learned to choose the most fitting methodologies for the business problems you encounter.What you will learnUnderstand core concepts of mathematical models and their relevance in solving problemsExplore various approaches to modeling and learning using PythonWork with tested mathematical tools to gather meaningful insightsBlend mathematical modeling with machine learning to find optimal solutions to business problemsOptimize ML models built with business data, apply them to understand their impact on the business, and address critical questionsApply mathematical optimization for data-scarce problems where the objective and constraints are knownWho this book is forIf you are a budding data scientist seeking to augment your journey with mathematics, this book is for you. Researchers and R&D scientists will also be able to harness the concepts covered to their full potential. To make the best use of this book, a background in linear algebra, differential equations, basics of statistics, data types, data structures, and numerical algorithms will be useful.
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Introduction to Generative AI with Julia and Python
Elevate your coding capabilities and gain the skills necessary for maximized performance and career opportunities in the field of Artificial Intelligence. This book is a step-by-step guide designed to equip software engineers with solid foundation of Generative AI basics. The book begins by introducing Generative AI using Google Colab to establish a solid foundation, before demonstrating how to leverage Python and Julia's AI capabilities to construct Artificial Neural Networks. You'll then explore advanced AI models, such as variational autoencoders, in order to generative images and text. Author Pierluigi Riti not only navigates the complexities of this emerging topic but fills a crucial gap by guiding software engineers through theory before leading them to produce different types of models using only free resources. What You Will Learn Acquire a solid foundation of Generative AI basics.Utilize the capabilities of Python and Julia to constructure Artificial Neural NetworksExplore advanced AI models including variational autoencoders, to generate images Survey different architectures for AI and utilize free resources to produce different AI Who This Book Is For Programmers new to artificial intelligence, but with a fundamental grasp of Python.
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Computer Programming for Absolute Beginners
Get to grips with the building blocks of programming languages and get started on your programming journey without a computer science degreeKey FeaturesUnderstand the fundamentals of a computer program and apply the concepts you learn to different programming languagesGain the confidence to write your first computer programExplore tips, techniques, and best practices to start coding like a professional programmerBook DescriptionLearning how to code has many advantages, and gaining the right programming skills can have a massive impact on what you can do with your current skill set and the way you advance in your career. This book will be your guide to learning computer programming easily, helping you overcome the difficulties in understanding the major constructs in any mainstream programming language.Computer Programming for Absolute Beginners starts by taking you through the building blocks of any programming language with thorough explanations and relevant examples in pseudocode. You'll understand the relationship between computer programs and programming languages and how code is executed on the computer. The book then focuses on the different types of applications that you can create with your programming knowledge. You'll delve into programming constructs, learning all about statements, operators, variables, and data types. As you advance, you'll see how to control the flow of your programs using control structures and reuse your code using functions. Finally, you'll explore best practices that will help you write code like a pro.By the end of this book, you'll be prepared to learn any programming language and take control of your career by adding coding to your skill set.What you will learnGet to grips with basic programming language concepts such as variables, loops, selection and functionsUnderstand what a program is and how the computer executes itExplore different programming languages and learn about the relationship between source code and executable codeSolve problems using various paradigms such as procedural programming, object oriented programming, and functional programmingWrite high-quality code using several coding conventions and best practicesBecome well-versed with how to track and fix bugs in your programsWho this book is forThis book is for beginners who have never programmed before and are looking to enter the world of programming. This includes anyone who is about to start studying programming and wants a head start, or simply wants to learn how to program on their own.
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Handbook of Logic in Artificial Intelligence and Logic Programming: Volume 1: Logic Foundations
Logic is now widely recognized to be one of the foundational disciplines of computing and has found applications in virtually all aspects of the subject, from software engineering and hardware to programming languages and artificial intelligence. This Handbook has been created in response to the growing need for an in-depth survey of the applications of logic in AI and computer science.The Handbook of Logic in Artificial Intelligence and Logic Programming will be published in five volumes. Each volume provides a combination of authoritative exposition, comprehensive survey, and fundamental research exploring the underlying unifying themes in the various areas. this first volume presents the logical foundations and deduction methodologies extensively used in AI.
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R 489.00
Electrodynamics Tutorials with Python Simulations
This book provides an accessible introduction to intermediate-level electrodynamics with computational approaches to complement a traditional mathematical treatment of the subject. It covers key topics in electrodynamics, such as electromagnetic fields, forces, potentials, and waves as well as Special Theory of Relativity.Through intuition-building examples and visualizations in the Python programming language, it helps readers to develop technical computing skills in numerical and symbolic calculations, modeling and simulations, and visualizations. Python is a highly readable and practical programming language, making this book appropriate for students without extensive programming experience.This book can serve as an electrodynamics textbook for undergraduate physics and engineering students in their second or third years, who are studying intermediate- or advanced-level electrodynamics and who want to learn techniques for scientific computing at the same time. This book will also appeal to computer science students who want to see how their computer programming skills may be applied to science, particularly to physics, without needing too much background physics knowledge.Key features● Major concepts in classical electrodynamics are introduced cohesively through computational and mathematical treatments.● Computational examples in Python programming language guide students on how to simulate and visualize electrodynamic principles and phenomena for themselves.Taejoon Kouh is a Professor of Nano and Electronic Physics at Kookmin University, Republic of Korea. He earned his B.A. in physics from Boston University and Sc.M. and Ph.D. degrees in physics from Brown University. After his study in Providence, RI, he returned to Boston, MA, and worked as a postdoctoral research associate in the Department of Aerospace and Mechanical Engineering at Boston University. He is a full faculty member in the Department of Nano and Electronic Physics at Kookmin University in Seoul, Korea, teaching and supervising undergraduate and graduate students. His current research involves the dynamics of nanoelectromechanical systems and the development of fast and reliable transduction methods and innovative applications based on tiny motion.Minjoon Kouh is a program scientist for a philanthropic initiative. He was a Professor of Physics and Neuroscience at Drew University, USA, where he taught more than 30 distinct types of courses. He holds Ph.D. and B.S. degrees in physics from MIT and an M.A. from UC Berkeley. He completed a postdoctoral research fellowship at the Salk Institute for Biological Studies in La Jolla, CA. His research includes computational modeling of the primate visual cortex, information-theoretic analysis of neural responses, machine learning, and pedagogical innovations in undergraduate science education.
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Handbook of Computer Animation
Written by specialists in teaching computer animation, this text addresses key international topics of computer animation, such as: mathematics, modelling, rendering, and compositing. Each chapter discusses a particular topic and how it is applied, including state-of-the-art techniques that are used in computer animation. The handbook provides a complete and up-to-date picture of computer animation and will be a valuable reference source for programmers, technical directors and animators in computer animation, computer games and special effects and also undergraduate and postgraduate students. The editor, John Vince, has written and edited over 20 books on computer graphics, computer animation and virtual reality.
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R 702.00
Math for Programming
This book summarizes all the core mathematical topics a typical professional software engineer needs to know. In condensing the various concepts covered in an undergraduate computer science program into a single volume, it provides an excellent starting point for independent study, or a refresher for those who haven't been in a classroom for years. Early chapters cover preliminary subjects like number representation systems, set theory, and Boolean algebra, followed by a dive into the field of discrete mathematics, including functions, induction proofs, number theory, combinatorics, graphs, and trees. Later sections examine essential topics in probability, statistics, linear algebra, and calculus. Rather than confine itself to abstract theory, the book focuses on practical applications and numerical methods at the level typically encountered by working software developers. In addition, hands-on code examples in Python and C make the topics concrete.
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Handbook of Logic in Artificial Intelligence and Logic Programming: Volume 2: Deduction Methodologies
Logic is now widely recognized to be one of the foundational disciplines of computing and has found applications in virtually all aspects of the subject, from software engineering and hardware to programming languages and artificial intelligence. There is a growing need for an in-depth survey of the applications of logic in Al and computer science. The Handbook of Logic in Articial Intelligence and Logic Programming and its companion, the Handbook of Logic in Computer Science, have been created in response to this need. We see the creation of the Handbook as a combination of authoritative exposition, comprehensive survey, and fundamental research exploring the underlying themes in the various areas. The intended audience is graduate students and researchers in the areas of A1 and logic, as well as other people interested in the subject. We assume as background some mathematical sophistication. Much of the material will be of interest to logicians and mathematicians. The tables of contents of the volumes were finalized after extensive discussions between handbook authors and second readers. This book is intended for theoretical computer scientists; logicians.Volume Co-ordinator:: S
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