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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
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 2,454.00
Python for Mathematics
Python for Mathematics introduces readers to effective methods for doing mathematics using the Python programming language. Most programming texts introduce readers to the building blocks of programming and build up to using more sophisticated tools for a specific purpose, like doing particular mathematical tasks. This is akin to teaching someone how to forge metal so as to make a nail, and then slowly working up to using sophisticated power tools so as to actually build something. This book does things in a different way, by first getting readers to begin using and understanding the tools that are going to be helpful to them as mathematicians, and only then moving onto the granular details. In this way, the practical application of the tools can aid in the understanding of the theory.Features· Complete with engaging, practical exercises· Many useful and detailed coding examples· Suitable for undergraduates in mathematics, and other quantitative sciences· Empowers readers to design and create their own Python tools.
R 10,134.08
Python for Mathematics
Python for Mathematics introduces readers to effective methods for doing mathematics using the Python programming language. Most programming texts introduce readers to the building blocks of programming and build up to using more sophisticated tools for a specific purpose, like doing particular mathematical tasks. This is akin to teaching someone how to forge metal so as to make a nail, and then slowly working up to using sophisticated power tools so as to actually build something. This book does things in a different way, by first getting readers to begin using and understanding the tools that are going to be helpful to them as mathematicians, and only then moving onto the granular details. In this way, the practical application of the tools can aid in the understanding of the theory.Features· Complete with engaging, practical exercises· Many useful and detailed coding examples· Suitable for undergraduates in mathematics, and other quantitative sciences· Empowers readers to design and create their own Python tools.
R 2,711.00
Mathematics and Python Programming
This book contains 120 Python programs and more than 110 illustrations useful both to students of science of the first university courses, as well as high school students and teachers. All the programs and graphics of the book have been developed using free software, which you can obtain free and legally on the internet, so it is not necessary to buy any software. Python is currently the most popular language for teaching introductory computer science courses at top-ranked U.S. universities. You can freely write Python programs whether you are a Windows user or whether you prefer Mac or Linux. A free pdf excerpt is available at our website www.pysamples.com. The buyers of the book can also download the code of the Python programs. The book consists of a detailed introduction to Python, followed by ten chapters of mathematics with its corresponding Python programs, results and graphs.
R 807.68
R 3,114.00
Mathematical Methods using Python
This advanced undergraduate textbook presents a new approach to teaching mathematical methods for scientists and engineers. It provides a practical, pedagogical introduction to utilizing Python in Mathematical and Computational Methods courses. Both analytical and computational examples are integrated from its start. Each chapter concludes with a set of problems designed to help students hone their skills in mathematical techniques, computer programming, and numerical analysis. The book places less emphasis on mathematical proofs, and more emphasis on how to use computers for both symbolic and numerical calculations. It contains 182 extensively documented coding examples, based on topics that students will encounter in their advanced courses in Mechanics, Electronics, Optics, Electromagnetism, Quantum Mechanics etc.An introductory chapter gives students a crash course in Python programming and the most often used libraries (SymPy, NumPy, SciPy, Matplotlib). This is followed by chapters dedicated to differentiation, integration, vectors and multiple integration techniques. The next group of chapters covers complex numbers, matrices, vector analysis and vector spaces. Extensive chapters cover ordinary and partial differential equations, followed by chapters on nonlinear systems and on the analysis of experimental data using linear and nonlinear regression techniques, Fourier transforms, binomial and Gaussian distributions. The book is accompanied by a dedicated GitHub website, which contains all codes from the book in the form of ready to run Jupyter notebooks. A detailed solutions manual is also available for instructors using the textbook in their courses.Key Features:A unique teaching approach which merges mathematical methods and the Python programming skills which physicists and engineering students need in their coursesUses examples and models from physical and engineering systems, to motivate the mathematics being taughtStudents learn to solve scientific problems in three different ways: traditional pen-and-paper methods, using scientific numerical techniques with NumPy and SciPy, and using Symbolic Python (SymPy).
R 2,711.00
Symbolic Mathematics with Python
This book provides a hands-on approach to computer symbolic computation using the elementary commands of Python. Symbolic computer mathematics is the study of algorithms that can be implemented as computer programs which provide exact results rather than numerical approximations. The author begins by discussing Python essentials an number theory. Then, the book covers the simplification and evaluation of expressions involving multivariate rational functions, the exact solutions of systems of linear equations, and applications of polynomial algebra. Programs in symbolic differentiation and indefinite integration programs are also developed.
R 2,450.37
R 764.00
Matematicas y Programacion En Python (2* Edicion)
El libro contiene 120 programas originales de Python y mas de 110 ilustraciones. Esperamos que sea util tanto a estudiantes de bachillerato, como a estudiantes de ciencias de los primeros cursos universitarios, asi como recurso pedagogico para los profesores. Todos los programas y graficas del libro se han elaborado utilizando software libre, que se puede obtener gratuita y legalmente en internet, por lo que no es necesario adquirir ningun software adicional. El lenguaje Python es especialmente adecuado para el aprendizaje de programacion, y actualmente es el lenguaje mas utilizado como lenguaje de introduccion a la programacion en las universidades de Estados Unidos. Se puede programar en Python tanto si eres usuario de Windows como si prefieres Mac o Linux. Puedes descargar gratuitamente un extracto de mas de 70 paginas del libro en formato pdf desde nuestro sitio web www.pysamples.com. Los compradores del libro tambien pueden descargar gratuitamente el codigo de los programas del libro.
R 990.96
Programming for Computations - Python
This book is published open access under a CC BY 4.0 license. This book presents computer programming as a key method for solving mathematical problems. This second edition of the well-received book has been extensively revised: All code is now written in Python version 3.6 (no longer version 2.7). In addition, the two first chapters of the previous edition have been extended and split up into five new chapters, thus expanding the introduction to programming from 50 to 150 pages. Throughout the book, the explanations provided are now more detailed, previous examples have been modified, and new sections, examples and exercises have been added. Also, a number of small errors have been corrected. The book was inspired by the Springer book TCSE 6: A Primer on Scientific Programming with Python (by Langtangen), but the style employed is more accessible and concise, in keeping with the needs of engineering students. The book outlines the shortest possible path from no previous experience with programming to a set of skills that allows students to write simple programs for solving common mathematical problems with numerical methods in the context of engineering and science courses. The emphasis is on generic algorithms, clean program design, the use of functions, and automatic tests for verification.
R 2,198.00
Introduction to Scientific Programming with Python
This open access book offers an initial introduction to programming for scientific and computational applications using the Python programming language. The presentation style is compact and example-based, making it suitable for students and researchers with little or no prior experience in programming. The book uses relevant examples from mathematics and the natural sciences to present programming as a practical toolbox that can quickly enable readers to write their own programs for data processing and mathematical modeling. These tools include file reading, plotting, simple text analysis, and using NumPy for numerical computations, which are fundamental building blocks of all programs in data science and computational science. At the same time, readers are introduced to the fundamental concepts of programming, including variables, functions, loops, classes, and object-oriented programming. Accordingly, the book provides a sound basis for further computer science and programming studies.
R 1,392.00
Mathematical Logic through Python
Using a unique pedagogical approach, this text introduces mathematical logic by guiding students in implementing the underlying logical concepts and mathematical proofs via Python programming. This approach, tailored to the unique intuitions and strengths of the ever-growing population of programming-savvy students, brings mathematical logic into the comfort zone of these students and provides clarity that can only be achieved by a deep hands-on understanding and the satisfaction of having created working code. While the approach is unique, the text follows the same set of topics typically covered in a one-semester undergraduate course, including propositional logic and first-order predicate logic, culminating in a proof of Gödel's completeness theorem. A sneak peek to Gödel's incompleteness theorem is also provided. The textbook is accompanied by an extensive collection of programming tasks, code skeletons, and unit tests. Familiarity with proofs and basic proficiency in Python is assumed.
R 3,087.35
Applied Engineering Mathematics with MATLAB® and Python
This comprehensive new textbook bridges the gap between mathematical theory and practical engineering applications. Organized into eight chapters, it introduces programming skills in MATLAB and Python alongside essential mathematical concepts, progressing from basic computation and linear algebra to more advanced topics such as differential equations, Fourier series, and Laplace transforms. The book develops both mathematical reasoning and computational proficiency, equipping students with essential tools for modern engineering practice. It includes numerous MATLAB and Python scripts, presented within the text and available through a dedicated GitHub repository for hands-on learning and reproducibility. Each chapter concludes with problems—many with solutions—that reinforce understanding and encourage practical application. By integrating theory with coding exercises, the book ensures that students not only grasp key concepts but also gain the skills to apply them in real-world contexts. The book is intended for undergraduate engineering students in Civil, Mechanical, Electrical, and Chemical Engineering. It is particularly suited for modules in Engineering Mathematics, Programming, Computations, and Analytical Studies, and can also serve as a supplementary reference for postgraduate students seeking applied mathematical and computational skills. Instructors will find it a valuable teaching resource that integrates theory with practice, adaptable for both semester- and year-long courses.The GitHub repository can be accesses at https://github.com/prog871/CRC-book2025.
R 4,633.00
Applied Math with Python
A step-by-step guide for using Python to transform abstract mathematical concepts into effective, on-the-ground scripts that solve real-world business problems Applied Math with Python: Solve Real-World Problems with Python-Based Solutions is a detailed, step-by-step guide for business professionals, analysts, and data scientists interested in using Python to perform crucial organizational tasks: optimizing inefficient supply chains, calculating probabilities, forecasting financial performance, mining customer data for new insights, and more. Author, researcher, and Assistant Professor of Finance at the University of North Florida, Blake Rayfield uses practical examples and hands-on exercises to explain how to combine concepts from optimization, probability, statistics, and other branches of mathematics with the Python language to solve difficult, common business problems. You’ll discover how marketing managers can use Python to create useful customer segments, how to model revenue growth, and how to allocate limited resources in a product launch or expansion. Inside the book: Modular, plug-and-play strategies for solving hard problems in Python in situations where a spreadsheet is inadequateInstructions for building effective, scalable Python scripts incorporating many of the most powerful Python libraries, including pandas, NumPy, matplotlib, seaborn, scikit-learn, and PlotlyStart-to-finish coverage for business professionals – from building a Python scripting environment on your local computer or in a cloud environment to designing, writing, testing, and running a functional script Perfect for entrepreneurs, analysts, managers, and professionals working in AI, data science, and finance, Applied Math with Python is an expert guide for transforming abstract mathematical concepts into useful, repeatable, scalable solutions you can put to work immediately in your team and in your organization.
R 1,128.00
Python for Scientists
The third edition of this practical introduction to Python has been thoroughly updated, with all code migrated to Jupyter notebooks. The notebooks are available online with executable versions of all of the book's content (and more). The text starts with a detailed introduction to the basics of the Python language, without assuming any prior knowledge. Building upon each other, the most important Python packages for numerical math (NumPy), symbolic math (SymPy), and plotting (Matplotlib) are introduced, with brand new chapters covering numerical methods (SciPy) and data handling (Pandas). Further new material includes guidelines for writing efficient Python code and publishing code for other users. Simple and concise code examples, revised for compatibility with Python 3, guide the reader and support the learning process throughout the book. Readers from all of the quantitative sciences, whatever their background, will be able to quickly acquire the skills needed for using Python effectively.
R 1,454.00
Mathematics And Programming For The Quantitative Economist - Volume 1: Fundamentals
This book covers calculus, linear algebra, probability and statistics, and computer programming (using Python) at the level required in strong, technically-oriented undergraduate and masters level economics programs. It is the first of a two-volume set, with the second volume focusing on mathematics and programming for networks and dynamic models. Together, the two volumes cover mathematics and programming used in economics, econometrics, data science, machine learning and artificial intelligence. It is intended to be used as a main text for mathematics for economics courses ranging from intermediate to masters levels, and as a resource or reference text for other economics, econometrics and data science courses. It should also be useful as a reference text for economics and data science practitioners who wish to learn more about the mathematics of these subjects.
R 6,155.00
R 3,358.00
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.
R 1,140.00
Mathematical Logic through Python
Using a unique pedagogical approach, this text introduces mathematical logic by guiding students in implementing the underlying logical concepts and mathematical proofs via Python programming. This approach, tailored to the unique intuitions and strengths of the ever-growing population of programming-savvy students, brings mathematical logic into the comfort zone of these students and provides clarity that can only be achieved by a deep hands-on understanding and the satisfaction of having created working code. While the approach is unique, the text follows the same set of topics typically covered in a one-semester undergraduate course, including propositional logic and first-order predicate logic, culminating in a proof of Gödel's completeness theorem. A sneak peek to Gödel's incompleteness theorem is also provided. The textbook is accompanied by an extensive collection of programming tasks, code skeletons, and unit tests. Familiarity with proofs and basic proficiency in Python is assumed.
R 1,311.25
Python Essentials
This text describes the essentials of the Python programming language for symbolic and numeric engineering problems, emphasizing an interactive approach. It incorporates the Spyder IDE in conjunction with the four Python packages (NumPy, SciPy, SymPy, and MatplotLib) most frequently used in engineering and the sciences. The book is divided into four parts. The first part, chapter 1, outlines the environment for using Python in an interactive mode and provides descriptions of the many functions and operations available in that environment for numerical and symbolic problem-solving and for basic 2-D plotting of results. For users with no or little experience with Python the chapter can serve as a tutorial while for users with some Python experience the chapter can serve as a reference guide. The second part of the book, contained in chapters 2 and 3, discusses vectors and arrays (matrices) from both a mathematical perspective as well as from a physical perspective. Engineering examples are provided in the chapters and the exercises. Chapters 3, 4, 5 and 8 are the third part of the book, covering the solution of 1) linear simultaneous algebraic equations, 2) nonlinear algebraic equations, 3) ordinary differential equations, and 4) partial differential equations, respectively. These topics again are covered from both a mathematical and physical standpoint with solutions obtained in engineering examples and exercises. Python animations are also introduced as part of the solutions found for ordinary differential equations. Finally, the fourth part of the book, chapter 7, covers the use of 2-D and 3-D plotting as well as symbolic plotting in detail, expanding significantly on the short introduction given in chapter 1.
R 2,406.00
Python for Scientific Computing and Artificial Intelligence
Python for Scientific Computing and Artificial Intelligence is split into 3 parts: in Section 1, the reader is introduced to the Python programming language and shown how Python can aid in the understanding of advanced High School Mathematics. In Section 2, the reader is shown how Python can be used to solve real-world problems from a broad range of scientific disciplines. Finally, in Section 3, the reader is introduced to neural networks and shown how TensorFlow (written in Python) can be used to solve a large array of problems in Artificial Intelligence (AI).This book was developed from a series of national and international workshops that the author has been delivering for over twenty years. The book is beginner friendly and has a strong practical emphasis on programming and computational modelling.Features:No prior experience of programming is requiredOnline GitHub repository available with codes for readers to practiceCovers applications and examples from biology, chemistry, computer science, data science, electrical and mechanical engineering, economics, mathematics, physics, statistics and binary oscillator computingFull solutions to exercises are available as Jupyter notebooks on the WebSupport MaterialGitHub Repository of Python Files and Notebooks: https://github.com/proflynch/CRC-Press/Solutions to All Exercises:Section 1: An Introduction to Python: https://drstephenlynch.github.io/webpages/SolutionsSection1.htmlSection 2: Python for Scientific Computing: https://drstephenlynch.github.io/webpages/SolutionsSection2.htmlSection 3: Artificial Intelligence: https://drstephenlynch.github.io/webpages/SolutionsSection3.html
R 3,041.00
R 5,735.90
Computational Mathematics
This textbook is a comprehensive introduction to computational mathematics and scientific computing suitable for undergraduate and postgraduate courses. It presents both practical and theoretical aspects of the subject, as well as advantages and pitfalls of classical numerical methods alongside with computer code and experiments in Python. Each chapter closes with modern applications in physics, engineering, and computer science. No previous experience in Python is required Simplified computer code for fast-paced learning and transferable skills development Includes practical problems ideal for project assignments and distance learning Presents both intuitive and rigorous faces of modern scientific computing Provides an introduction to neural networks and machine learning
R 6,766.87
Applied Mathematics with Open-Source Software
Applied Mathematics with Open-source Software: Operational Research Problems with Python and R is aimed at a broad segment of readers who wish to learn how to use open-source software to solve problems in applied mathematics. The book has an innovative structure with 4 sections of two chapters covering a large range of applied mathematical techniques: probabilistic modelling, dynamical systems, emergent behaviour and optimisation. The pairs of chapters in each section demonstrate different families of solution approaches. Each chapter starts with a problem, gives an overview of the relevant theory, shows a solution approach in R and in Python, and finally gives wider context by including a number of published references. This structure will allow for maximum accessibility, with minimal prerequisites in mathematics or programming as well as giving the right opportunities for a reader wanting to delve deeper into a particular topic.Features An excellent resource for scholars of applied mathematics and operational research, and indeed any academics who want to learn how to use open-source software. Offers more general and accessible treatment of the subject than other texts, both in terms of programming language but also in terms of the subjects considered. The R and Python sections purposefully mirror each other so that a reader can read only the section that interests them. An accompanying open-source repository with source files and further examples is posted online at https://bit.ly/3kpoKSd.
R 2,938.29
Python for Probability, Statistics, and Machine Learning
Using a novel integration of mathematics and Python codes, this book illustrates the fundamental concepts that link probability, statistics, and machine learning, so that the reader can not only employ statistical and machine learning models using modern Python modules, but also understand their relative strengths and weaknesses. To clearly connect theoretical concepts to practical implementations, the author provides many worked-out examples along with "Programming Tips" that encourage the reader to write quality Python code. The entire text, including all the figures and numerical results, is reproducible using the Python codes provided, thus enabling readers to follow along by experimenting with the same code on their own computers. Modern Python modules like Pandas, Sympy, Scikit-learn, Statsmodels, Scipy, Xarray, Tensorflow, and Keras are used to implement and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, interpretability, and regularization. Many abstract mathematical ideas, such as modes of convergence in probability, are explained and illustrated with concrete numerical examples. This book is suitable for anyone with undergraduate-level experience with probability, statistics, or machine learning and with rudimentary knowledge of Python programming.
R 2,564.00
Computational Framework for the Finite Element Method in MATLAB® and Python
Computational Framework for the Finite Element Method in MATLAB® and Python aims to provide a programming framework for coding linear FEM using matrix-based MATLAB® language and Python scripting language. It describes FEM algorithm implementation in the most generic formulation so that it is possible to apply this algorithm to as many application problems as possible. Readers can follow the step-by-step process of developing algorithms with clear explanations of its underlying mathematics and how to put it into MATLAB and Python code. The content is focused on aspects of numerical methods and coding FEM rather than FEM mathematical analysis. However, basic mathematical formulations for numerical techniques which are needed to implement FEM are provided. Particular attention is paid to an efficient programming style using sparse matrices. Features Contains ready-to-use coding recipes allowing fast prototyping and solving of mathematical problems using FEM Suitable for upper-level undergraduates and graduates in applied mathematics, science or engineering Both MATLAB and Python programming codes are provided to give readers more flexibility in the practical framework implementation
R 7,561.63
Mathematics for Game Programming and Computer Graphics
A comprehensive guide to learning fundamental 3D mathematical principles used in games and computer graphics by exampleKey FeaturesGet acquainted with the essential mathematics needed to describe, simulate, and render 3D creationsConstruct and manipulate 3D animated environments using Python, Pygame, and PyOpenGLDevelop vertex and fragment shaders in OpenGL shader language to speed up renderingBook DescriptionMathematics is an essential skill when it comes to graphics and game development, particularly if you want to understand the generation of real-time computer graphics and the manipulation of objects and environments in a detailed way. Python, together with Pygame and PyOpenGL, provides you with the opportunity to explore these features under the hood, revealing how computers generate and manipulate 3D environments.Mathematics for Game Programming and Computer Graphics is an exhaustive guide to getting “back to the basics” of mathematics, using a series of problem-based, practical exercises to explore ideas around drawing graphic lines and shapes, applying vectors and vertices, constructing and rendering meshes, and working with vertex shaders. By leveraging Python, Pygame, and PyOpenGL, you'll be able to create your own mathematics-based engine and API that will be used throughout to build applications.By the end of this graphics focussed book, you'll have gained a thorough understanding of how essential mathematics is for creating, rendering, and manipulating 3D virtual environments and know the secrets behind today's top graphics and game engines.What you will learnGet up and running with Python, Pycharm, Pygame, and PyOpenGLExperiment with different graphics API drawing commandsReview basic trigonometry and how it's important in 3D environmentsApply vectors and matrices to move, orient, and scale 3D objectsRender 3D objects with textures, colors, shading, and lightingWork with vertex shaders for faster GPU-based renderingWho this book is forThis book is for programmers who want to enhance their 3D mathematics skills relating to computer graphics and computer games. Knowledge of high school–level mathematics and a working understanding in an object-orientated language is needed to grasp the contents present in this book.
R 1,791.00
Essential Math for Physics Undergraduates
Essential Math for Physics Undergraduates: Practical Examples with Python Tools covers basic applied Math and entry-level programming. A gap often exists in the Physics student’s Math curriculum, between the Math taught by the Math departments and the Math taught either in a “Math Methods” or advanced Physics courses, such as introduction to quantum mechanics, classical mechanics, thermal and statistical physics, and electromagnetic theory. This text plugs that gap and provides the new Physics student with the necessary tools to succeed in their Physics curriculum. The book helps instructors to fill in the gap between what new Physics students learn in high school and introductory college math courses, providing them with what they need to know as they progress through their Physics curriculum.Key Features:Covers Math that is essential in Physics curricula.Offers a more accessible introduction to Math than can be found in typical “Math Methods for Physicists” texts.Introduces Python and shows the reader how to use it for basic math problems.
R 5,165.00
Essential Math for Physics Undergraduates
Essential Math for Physics Undergraduates: Practical Examples with Python Tools covers basic applied Math and entry-level programming. A gap often exists in the Physics student’s Math curriculum, between the Math taught by the Math departments and the Math taught either in a “Math Methods” or advanced Physics courses, such as introduction to quantum mechanics, classical mechanics, thermal and statistical physics, and electromagnetic theory. This text plugs that gap and provides the new Physics student with the necessary tools to succeed in their Physics curriculum. The book helps instructors to fill in the gap between what new Physics students learn in high school and introductory college math courses, providing them with what they need to know as they progress through their Physics curriculum.Key Features:Covers Math that is essential in Physics curricula.Offers a more accessible introduction to Math than can be found in typical “Math Methods for Physicists” texts.Introduces Python and shows the reader how to use it for basic math problems.
R 1,862.10
Introduction to Python Programming for Scientists and Engineers
Python is one of the most popular programming languages, widely used for data analysis and modelling, and is fast becoming the leading choice for scientists and engineers. Unlike other textbooks introducing Python, typically organised by language syntax, this book uses many examples from across Biology, Chemistry, Physics, Earth science, and Engineering to teach and motivate students in science and engineering. The text is organised by the tasks and workflows students undertake day-to-day, helping them see the connections between programming tools and their disciplines. The pace of study is carefully developed for complete beginners, and a spiral pedagogy is used so concepts are introduced across multiple chapters, allowing readers to engage with topics more than once. “Try This!” exercises and online Jupyter notebooks encourage students to test their new knowledge, and further develop their programming skills. Online solutions are available for instructors, alongside discipline-specific homework problems across the sciences and engineering.
R 3,017.00
Numerical Methods in Engineering with Python 3
This book is an introduction to numerical methods for students in engineering. It covers solution of equations, interpolation and data fitting, solution of differential equations, eigenvalue problems and optimisation. The algorithms are implemented in Python 3, a high-level programming language that rivals MATLAB® in readability and ease of use. All methods include programs showing how the computer code is utilised in the solution of problems. The book is based on Numerical Methods in Engineering with Python, which used Python 2. This new edition demonstrates the use of Python 3 and includes an introduction to the Python plotting package Matplotlib. This comprehensive book is enhanced by the addition of numerous examples and problems throughout.
R 5,317.18
Python for MATLAB Development
MATLAB can run Python code!Python for MATLAB Development shows you how to enhance MATLAB with Python solutions to a vast array of computational problems in science, engineering, optimization, statistics, finance, and simulation. It is three books in one:A thorough Python tutorial that leverages your existing MATLAB knowledge with a comprehensive collection of MATLAB/Python equivalent expressionsA reference guide to setting up and managing a Python environment that integrates cleanly with MATLABA collection of recipes that demonstrate Python solutions invoked directly from MATLAB This book shows how to call Python functions to enhance MATLAB's capabilities. Specifically, you'll see how Python helps MATLAB:Run faster with numbaDistribute work to a compute cluster with daskFind symbolic solutions to integrals, derivatives, and series summations with SymPyOverlay data on maps with CartopySolve mixed-integer linear programming problems with PuLPInteract with Redis via pyredis, PostgreSQL via psycopg2, and MongoDB via pymongoRead and write file formats that are not natively understood by MATLAB, such as SQLite, YAML, and iniWho This Book Is ForMATLAB developers who are new to Python and other developers with some prior experience with MATLAB, R, IDL, or Mathematica.
R 1,596.00
Introduction to Engineering and Scientific Computing with Python
As more and more engineering departments and companies choose to use Python, this book provides an essential introduction to this open-source, free-to-use language. Expressly designed to support first-year engineering students, this book covers engineering and scientific calculations, Python basics, and structured programming.Based on extensive teaching experience, the text uses practical problem solving as a vehicle to teach Python as a programming language. By learning computing fundamentals in an engaging and hands-on manner, it enables the reader to apply engineering and scientific methods with Python, focusing this general language to the needs of engineers and the problems they are required to solve on a daily basis. Rather than inundating students with complex terminology, this book is designed with a leveling approach in mind, enabling students at all levels to gain experience and understanding of Python. It covers such topics as structured programming, graphics, matrix operations, algebraic equations, differential equations, and applied statistics. A comprehensive chapter on working with data brings this book to a close.This book is an essential guide to Python, which will be relevant to all engineers, particularly undergraduate students in their first year. It will also be of interest to professionals and graduate students looking to hone their programming skills, and apply Python to engineering and scientific contexts.
R 3,017.94
Mathematical Programming for Power Systems Operation
Explore the theoretical foundations and real-world power system applications of convex programming In Mathematical Programming for Power System Operation with Applications in Python, Professor Alejandro Garces delivers a comprehensive overview of power system operations models with a focus on convex optimization models and their implementation in Python. Divided into two parts, the book begins with a theoretical analysis of convex optimization models before moving on to related applications in power systems operations. The author eschews concepts of topology and functional analysis found in more mathematically oriented books in favor of a more natural approach. Using this perspective, he presents recent applications of convex optimization in power system operations problems. Mathematical Programming for Power System Operation with Applications in Python uses Python and CVXPY as tools to solve power system optimization problems and includes models that can be solved with the presented framework. The book also includes: A thorough introduction to power system operation, including economic and environmental dispatch, optimal power flow, and hosting capacityComprehensive explorations of the mathematical background of power system operation, including quadratic forms and norms and the basic theory of optimizationPractical discussions of convex functions and convex sets, including affine and linear spaces, politopes, balls, and ellipsoidsIn-depth examinations of convex optimization, including global optimums, and first and second order conditions Perfect for undergraduate students with some knowledge in power systems analysis, generation, or distribution, Mathematical Programming for Power System Operation with Applications in Python is also an ideal resource for graduate students and engineers practicing in the area of power system optimization.
R 4,395.00
Python for Scientific Computing and Artificial Intelligence
Python for Scientific Computing and Artificial Intelligence is split into 3 parts: in Section 1, the reader is introduced to the Python programming language and shown how Python can aid in the understanding of advanced High School Mathematics. In Section 2, the reader is shown how Python can be used to solve real-world problems from a broad range of scientific disciplines. Finally, in Section 3, the reader is introduced to neural networks and shown how TensorFlow (written in Python) can be used to solve a large array of problems in Artificial Intelligence (AI).This book was developed from a series of national and international workshops that the author has been delivering for over twenty years. The book is beginner friendly and has a strong practical emphasis on programming and computational modelling.Features:No prior experience of programming is requiredOnline GitHub repository available with codes for readers to practiceCovers applications and examples from biology, chemistry, computer science, data science, electrical and mechanical engineering, economics, mathematics, physics, statistics and binary oscillator computingFull solutions to exercises are available as Jupyter notebooks on the WebSupport MaterialGitHub Repository of Python Files and Notebooks: https://github.com/proflynch/CRC-Press/Solutions to All Exercises:Section 1: An Introduction to Python: https://drstephenlynch.github.io/webpages/SolutionsSection1.htmlSection 2: Python for Scientific Computing: https://drstephenlynch.github.io/webpages/SolutionsSection2.htmlSection 3: Artificial Intelligence: https://drstephenlynch.github.io/webpages/SolutionsSection3.html
R 8,572.00
Climate Mathematics
This unique text provides a thorough, yet accessible, grounding in the mathematics, statistics, and programming that students need to master for coursework and research in climate science, meteorology, and oceanography. Assuming only high school mathematics, it presents carefully selected concepts and techniques in linear algebra, statistics, computing, calculus and differential equations within the context of real climate science examples. Computational techniques are integrated to demonstrate how to visualize, analyze, and apply climate data, with R code featured in the book and both R and Python code available online. Exercises are provided at the end of each chapter with selected solutions available to students to aid self-study and further solutions provided online for instructors only. Additional online supplements to aid classroom teaching include datasets, images, and animations. Guidance is provided on how the book can support a variety of courses at different levels, making it a highly flexible text for undergraduate and graduate students, as well as researchers and professional climate scientists who need to refresh or modernize their quantitative skills.
R 2,707.00
Kernel Methods for Machine Learning with Math and Python
The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than relying on knowledge or experience. This textbook addresses the fundamentals of kernel methods for machine learning by considering relevant math problems and building Python programs. The book’s main features are as follows:The content is written in an easy-to-follow and self-contained style.The book includes 100 exercises, which have been carefully selected and refined. As their solutions are provided in the main text, readers can solve all of the exercises by reading the book.The mathematical premises of kernels are proven and the correct conclusions are provided, helping readers to understand the nature of kernels.Source programs and running examples are presented to help readers acquire a deeper understanding of the mathematics used.Once readers have a basic understanding of the functional analysis topics covered in Chapter 2, the applications are discussed in the subsequent chapters. Here, no prior knowledge of mathematics is assumed.This book considers both the kernel for reproducing kernel Hilbert space (RKHS) and the kernel for the Gaussian process; a clear distinction is made between the two.
R 2,015.00
Applied Mathematics with Open-Source Software
Applied Mathematics with Open-source Software: Operational Research Problems with Python and R is aimed at a broad segment of readers who wish to learn how to use open-source software to solve problems in applied mathematics. The book has an innovative structure with 4 sections of two chapters covering a large range of applied mathematical techniques: probabilistic modelling, dynamical systems, emergent behaviour and optimisation. The pairs of chapters in each section demonstrate different families of solution approaches. Each chapter starts with a problem, gives an overview of the relevant theory, shows a solution approach in R and in Python, and finally gives wider context by including a number of published references. This structure will allow for maximum accessibility, with minimal prerequisites in mathematics or programming as well as giving the right opportunities for a reader wanting to delve deeper into a particular topic.Features An excellent resource for scholars of applied mathematics and operational research, and indeed any academics who want to learn how to use open-source software. Offers more general and accessible treatment of the subject than other texts, both in terms of programming language but also in terms of the subjects considered. The R and Python sections purposefully mirror each other so that a reader can read only the section that interests them. An accompanying open-source repository with source files and further examples is posted online at https://bit.ly/3kpoKSd.
R 7,579.51
Practical Discrete Mathematics
A practical guide simplifying discrete math for curious minds and demonstrating its application in solving problems related to software development, computer algorithms, and data scienceKey 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 DescriptionDiscrete 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 forThis 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.
R 2,247.00
Hands-On GPU Programming with Python and CUDA
Build real-world applications with Python 2.7, CUDA 9, and CUDA 10. We suggest the use of Python 2.7 over Python 3.x, since Python 2.7 has stable support across all the libraries we use in this book.Key FeaturesExpand your background in GPU programming—PyCUDA, scikit-cuda, and NsightEffectively use CUDA libraries such as cuBLAS, cuFFT, and cuSolverApply GPU programming to modern data science applicationsBook DescriptionHands-On GPU Programming with Python and CUDA hits the ground running: you’ll start by learning how to apply Amdahl’s Law, use a code profiler to identify bottlenecks in your Python code, and set up an appropriate GPU programming environment. You’ll then see how to “query” the GPU’s features and copy arrays of data to and from the GPU’s own memory.As you make your way through the book, you’ll launch code directly onto the GPU and write full blown GPU kernels and device functions in CUDA C. You’ll get to grips with profiling GPU code effectively and fully test and debug your code using Nsight IDE. Next, you’ll explore some of the more well-known NVIDIA libraries, such as cuFFT and cuBLAS.With a solid background in place, you will now apply your new-found knowledge to develop your very own GPU-based deep neural network from scratch. You’ll then explore advanced topics, such as warp shuffling, dynamic parallelism, and PTX assembly. In the final chapter, you’ll see some topics and applications related to GPU programming that you may wish to pursue, including AI, graphics, and blockchain.By the end of this book, you will be able to apply GPU programming to problems related to data science and high-performance computing.What you will learnLaunch GPU code directly from PythonWrite effective and efficient GPU kernels and device functionsUse libraries such as cuFFT, cuBLAS, and cuSolverDebug and profile your code with Nsight and Visual ProfilerApply GPU programming to datascience problemsBuild a GPU-based deep neuralnetwork from scratchExplore advanced GPU hardware features, such as warp shufflingWho this book is forHands-On GPU Programming with Python and CUDA is for developers and data scientists who want to learn the basics of effective GPU programming to improve performance using Python code. You should have an understanding of first-year college or university-level engineering mathematics and physics, and have some experience with Python as well as in any C-based programming language such as C, C++, Go, or Java.
R 1,661.00
Python and Matplotlib Essentials for Scientists and Engineers
This book provides an introduction to the core features of the Python programming language and Matplotlib plotting routings for scientists and engineers (or students of either discipline) who want to use Python™ to analyse data, simulate physical processes, and render publication-quality plots. No previous programming experience is needed before reading the first page.Readers will learn the core features of the Python programming language in under a day. They will be able to immediately use Python to implement codes that solve their own problems and make beautiful plots and animations. Python code is extremely fast to prototype, allowing users to achieve results quickly and accurately. Python and Matplotlib Essentials for Scientists and Engineers is accessible for motivated high-school students, but will likely be most useful for undergraduate and graduate students as well as working professionals who have some background with the basic mathematical concepts. This book is intended for technical people who want to get things done.
R 1,464.00
Stochastic Processes Using Python
This unique introductory textbook provides a practical, pedagogical introduction to Monte Carlo Methods and Stochastic Processes. The book can be used for a wide variety of advanced undergraduate and first year graduate courses in Computational statistics, Introduction to stochastic processes, and Monte Carlo computational methods. The intended audience is advanced undergraduates and graduate students who take a stochastic processes course as well as researchers who need a practical introduction to Monte Carlo methods using Python.The textbook contains 146 well documented Python examples integrated within the text, which demonstrate how to use Python for both numerical and symbolic calculations of stochastic processes. Each chapter concludes with a set of problems designed to help readers hone their skills in coding stochastic processes.An introductory chapter introduces definitions and axioms of probability, conditional probability and Bayes’ rule, with applications of finite stochastic processes. This is followed by Monte Carlo estimation of integrals and probabilities, and the rejection sampling and the inverse CDF transformation techniques.Two chapters introduce simulations and sampling methods for all the classic continuous and discrete distributions, and example codes demonstrate applications of the central limit theorem. A detailed chapter is dedicated to variance reduction methods: stratified sampling, importance sampling, control and antithetic variables. Joint distributions are applied to Bayes’ theorem, conditional expectation and variance, the laws of total expectation and total variance and convolution of random variables. The next chapter presents detailed simulations of the important Bernoulli and Poisson processes and their properties: distribution of events, arrival times, waiting times and autocorrelation, in the general context of stationary processes. The next chapter is dedicated to examples of random walks, gambler’s ruin, the diffusion equation, and the solution of difference equations which leads to a discussion of Markov processes. Stochastic birth-death processes are presented next, with coded examples of the master equation, Kolmogorov equations and the Gillespie algorithm.The book concludes with three detailed chapters on Markov chains, Monte Carlo (MCMC) methods and Bayesian Inference. Coded examples are given for transition matrices, stationary distributions, absorbing states, the Metropolis-Hastings algorithm, slice sampling, Gibbs sampling and Bayesian regression.Key Features:A unique practical approach which merges presentation of the basic theory and the practical Python programming examples.Contains 146 well documented Python codes, which demonstrate clearly the theoretical and mathematical concepts, without overwhelming readers with mathematical details.All Python codes are available for downloading in the book’s website at GitHub, in the form of ready to run Jupyter notebooks.Detailed PowerPoint presentations
R 5,165.00
Python and Matplotlib Essentials for Scientists and Engineers
This book provides an introduction to the core features of the Python programming language and Matplotlib plotting routings for scientists and engineers (or students of either discipline) who want to use Python™ to analyse data, simulate physical processes, and render publication-quality plots. No previous programming experience is needed before reading the first page.Readers will learn the core features of the Python programming language in under a day. They will be able to immediately use Python to implement codes that solve their own problems and make beautiful plots and animations. Python code is extremely fast to prototype, allowing users to achieve results quickly and accurately. The examples within the book are available for download.Python and Matplotlib Essentials for Scientists and Engineers is accessible for motivated high-school students, but will likely be most useful for undergraduate and graduate students as well as working professionals who have some background with the basic mathematical concepts. This book is intended for technical people who want to get things done.
R 1,466.00
Introduction to Python for Science and Engineering
Introduction to Python for Science and Engineering offers a quick and incisive introduction to the Python programming language for use in any science or engineering discipline. The approach is pedagogical and “bottom up,” which means starting with examples and extracting more general principles from that experience. No prior programming experience is assumed.Readers will learn the basics of Python syntax, data structures, input and output, conditionals and loops, user-defined functions, plotting, animation, and visualization. They will also learn how to use Python for numerical analysis, including curve fitting, random numbers, linear algebra, solutions to nonlinear equations, numerical integration, solutions to differential equations, and fast Fourier transforms.Readers learn how to interact and program with Python using JupyterLab and Spyder, two simple and widely used integrated development environments.All the major Python libraries for science and engineering are covered, including NumPy, SciPy, Matplotlib, and Pandas. Other packages are also introduced, including Numba, which can render Python numerical calculations as fast as compiled computer languages such as C but without their complex overhead.
R 2,429.09
