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Floating Point Numerics for Games and Simulations
Floating point is ubiquitous in computers, where it is the default way to represent non-integer numbers. However, few people understand it. We all see weird behavior sometimes, and many programmers treat it as a mystical and imprecise system of math that just works until it sometimes doesn’t. We hear that we shouldn’t trust floating point with money, we know that 0.1 + 0.2 does not equal 0.3, and “NaN” shows up in our logs when things break. We rarely hear why any of this is the case, and less about what to do about it.This book pulls back the veil on floating point and shows how this number system we program with every day works. It discusses how to leverage the number system for common calculations, particularly in graphics and simulations, and avoid pitfalls. Further, we will review methods that can give you either better performance or better accuracy on tasks like numerical integration and function approximation, so you can learn to make the right tradeoffs in your programs.This book builds upon a basic knowledge of calculus and linear algebra, working with illustrative examples that demonstrate concepts rather than relying on theoretical proofs. Along the way, we will learn why Minecraft has struggled with boat physics and what the heck John Carmack was thinking with Quake III’s infamous fast reciprocal square root algorithm. By the end of the book, you will be able to understand how to work with floating point in a practical sense, from tracking down and preventing error in small calculations to choosing numerical building blocks for complex 3D simulations.Gives insight into how and why floating-point math worksDescribes how floating-point error arises and how to avoid itSurveys numerical methods important to graphics and numerical simulationsIncludes modern techniques to apply to your numerical problemsShows how to hack the floating-point numbers to compute faster and more accurately
R 3,421.71
R 611.00
Modelling in Mathematical Programming
This book provides basic tools for learning how to model in mathematical programming, from models without much complexity to complex system models. It presents a unique methodology for the building of an integral mathematical model, as well as new techniques that help build under own criteria. It allows readers to structure models from the elements and variables to the constraints, a basic modelling guide for any system with a new scheme of variables, a classification of constraints and also a set of rules to model specifications stated as logical propositions, helping to better understand models already existing in the literature. It also presents the modelling of all possible objectives that may arise in optimization problems regarding the variables values.The book is structured to guide the reader in an orderly manner, learning of the components that the methodology establishes in an optimization problem. The system includes the elements, which are all the actors that participate in the system, decision activities that occur in the system, calculations based on the decision activities, specifications such as regulations, impositions or actions of defined value and objective criterion, which guides the resolution of the system.
R 4,553.27
AI and ML for Coders in Pytorch
Eager to learn AI and machine learning but unsure where to start? Laurence Moroney's hands-on, code-first guide demystifies complex AI concepts without relying on advanced mathematics. Designed for programmers, it focuses on practical applications using PyTorch, helping you build real-world models without feeling overwhelmed.From computer vision and natural language processing (NLP) to generative AI with Hugging Face Transformers, this book equips you with the skills most in demand for AI development today. You'll also learn how to deploy your models across the web and cloud confidently.Gain the confidence to apply AI without needing advanced math or theory expertiseDiscover how to build AI models for computer vision, NLP, and sequence modeling with PyTorchLearn generative AI techniques with Hugging Face Diffusers and Transformers
R 1,824.00
R 611.00
R 891.43
Python For Kids, 2nd Edition
Python for Kids brings Python to life and brings kids (and their parents) into the wonderful world of programming. Author Jason R. Briggs guides readers through the basics, experimenting with unique (and often hilarious) example programs that feature ravenous monsters, secret agents, thieving ravens, and more. Full-colour illustrations keep things fun and engaging throughout! This second edition has been completely updated and revised to reflect the latest Python version and programming practices, with new puzzles to inspire readers to take their code farther than ever before.
R 1,051.00
Hands-On Reactive Programming with Python
A comprehensive guide to help you understand the principles of Reactive and asynchronous programming and its benefitsKey FeaturesExplore the advantages of Reactive programmingUse concurrency and parallelism in RxPY to build powerful reactive applicationsDeploy and scale your reactive applications using DockerBook DescriptionReactive programming is central to many concurrent systems, but it’s famous for its steep learning curve, which makes most developers feel like they're hitting a wall. With this book, you will get to grips with reactive programming by steadily exploring various conceptsThis hands-on guide gets you started with Reactive Programming (RP) in Python. You will learn abouta the principles and benefits of using RP, which can be leveraged to build powerful concurrent applications. As you progress through the chapters, you will be introduced to the paradigm of Functional and Reactive Programming (FaRP), observables and observers, and concurrency and parallelism. The book will then take you through the implementation of an audio transcoding server and introduce you to a library that helps in the writing of FaRP code. You will understand how to use third-party services and dynamically reconfigure an application.By the end of the book, you will also have learned how to deploy and scale your applications with Docker and Traefik and explore the significant potential behind the reactive streams concept, and you'll have got to grips with a comprehensive set of best practices.What you will learnStructure Python code for better readability, testing, and performanceExplore the world of event-based programmingGrasp the use of the most common operators in RxUnderstand reactive extensions beyond simple examplesMaster the art of writing reusable componentsDeploy an application on a cloud platform with Docker and TraefikWho this book is forIf you are a Python developer who wants to learn Reactive programming to build powerful concurrent and asynchronous applications, this book is for you. Basic understanding of the Python language is all you need to understand the concepts covered in this book.
R 1,661.00
R 610.00
Probability and Statistics for Data Science
This self-contained guide introduces two pillars of data science, probability theory, and statistics, side by side, in order to illuminate the connections between statistical techniques and the probabilistic concepts they are based on. The topics covered in the book include random variables, nonparametric and parametric models, correlation, estimation of population parameters, hypothesis testing, principal component analysis, and both linear and nonlinear methods for regression and classification. Examples throughout the book draw from real-world datasets to demonstrate concepts in practice and confront readers with fundamental challenges in data science, such as overfitting, the curse of dimensionality, and causal inference. Code in Python reproducing these examples is available on the book's website, along with videos, slides, and solutions to exercises. This accessible book is ideal for undergraduate and graduate students, data science practitioners, and others interested in the theoretical concepts underlying data science methods.
R 2,697.51
Machine Learning with Python for Everyone
The Complete Beginner’s Guide to Understanding and Building Machine Learning Systems with Python Machine Learning with Python for Everyone will help you master the processes, patterns, and strategies you need to build effective learning systems, even if you’re an absolute beginner. If you can write some Python code, this book is for you, no matter how little college-level math you know. Principal instructor Mark E. Fenner relies on plain-English stories, pictures, and Python examples to communicate the ideas of machine learning. Mark begins by discussing machine learning and what it can do; introducing key mathematical and computational topics in an approachable manner; and walking you through the first steps in building, training, and evaluating learning systems. Step by step, you’ll fill out the components of a practical learning system, broaden your toolbox, and explore some of the field’s most sophisticated and exciting techniques. Whether you’re a student, analyst, scientist, or hobbyist, this guide’s insights will be applicable to every learning system you ever build or use. Understand machine learning algorithms, models, and core machine learning conceptsClassify examples with classifiers, and quantify examples with regressorsRealistically assess performance of machine learning systemsUse feature engineering to smooth rough data into useful formsChain multiple components into one system and tune its performanceApply machine learning techniques to images and textConnect the core concepts to neural networks and graphical modelsLeverage the Python scikit-learn library and other powerful tools Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
R 1,140.00
Mathematical Foundations of Programming Semantics
This is the latest in a series of proceedings of conferences on the Mathematical Foundations of Programming Semantics. The purpose of the series is to bring together mathematicians and theoretical computer scientists who share the common interests of working on problems related to programming language semantics. The purpose of the book is to bring into print as quickly as possible papers which reflect the state of research on the topics comprising this area. The intended audience for the book consists of those researchers and graduate students with an interest in the research areas which are related to those presented in the book: programming language semantics, including algebraic, denotational and operational semantics, logics of programs, specification techniques, etc., and the relevant areas of mathematics research, including category theory, domain theory, ordered structures and lattice theory, and metric space methods. The papers included in the book represent the latest results in various facets of this rather broad research area, and this is the first time some of the ideas contained in these works are appearing in print.
R 2,015.00
Advanced Python Scripting for ArcGIS Pro
Tackle complex spatial data tasks effortlessly with this easy-to-follow guide to writing specialized Python scripts and developing tools for spatial data in ArcGIS® Pro.Advanced Python Scripting for ArcGIS Pro follows up on the topics explained in Python Scripting for ArcGIS Pro (Esri Press, 2024) and is now updated for ArcGIS Pro 3.2.Intended for users who have a good foundation in Python, this book explores how to develop scripts into tools and notebooks to share with others, use third-party packages, and learn other more specialized tasks. By the end of this book, you’ll be confident in writing more advanced scripts, developing them into tools and notebooks, and sharing them with others.The key topics you will learn include:Creating custom functions and classesWriting specialized scripts using ArcPy™Creating Python script tools and Python toolboxesSharing scripts and toolsManaging Python packages and environmentsMigrating scripts from Python 2 to 3NumPy, Pandas, and MatplotlibCreating and using notebooksArcGIS API for Python and Jupyter NotebookHelpful points to remember, key terms, and review questions are included at the end of each chapter to reinforce your understanding of Python. Companion data and tutorials are available online.Packed with advanced techniques and practical examples, Advanced Python Scripting for ArcGIS Pro is perfect for more experienced ArcGIS Pro users who are looking to upgrade their Python skills and enhance their workflows.
R 4,274.00
Python for R Users
The definitive guide for statisticians and data scientists who understand the advantages of becoming proficient in both R and Python The first book of its kind, Python for R Users: A Data Science Approach makes it easy for R programmers to code in Python and Python users to program in R. Short on theory and long on actionable analytics, it provides readers with a detailed comparative introduction and overview of both languages and features concise tutorials with command-by-command translations—complete with sample code—of R to Python and Python to R. Following an introduction to both languages, the author cuts to the chase with step-by-step coverage of the full range of pertinent programming features and functions, including data input, data inspection/data quality, data analysis, and data visualization. Statistical modeling, machine learning, and data mining—including supervised and unsupervised data mining methods—are treated in detail, as are time series forecasting, text mining, and natural language processing. • Features a quick-learning format with concise tutorials and actionable analytics • Provides command-by-command translations of R to Python and vice versa • Incorporates Python and R code throughout to make it easier for readers to compare and contrast features in both languages • Offers numerous comparative examples and applications in both programming languages • Designed for use for practitioners and students that know one language and want to learn the other • Supplies slides useful for teaching and learning either software on a companion website Python for R Users: A Data Science Approach is a valuable working resource for computer scientists and data scientists that know R and would like to learn Python or are familiar with Python and want to learn R. It also functions as textbook for students of computer science and statistics. A. Ohri is the founder of Decisionstats.com and currently works as a senior data scientist. He has advised multiple startups in analytics off-shoring, analytics services, and analytics education, as well as using social media to enhance buzz for analytics products. Mr. Ohri's research interests include spreading open source analytics, analyzing social media manipulation with mechanism design, simpler interfaces for cloud computing, investigating climate change and knowledge flows. His other books include R for Business Analytics and R for Cloud Computing.
R 2,441.00
R 387.37
Computational Experiment Approach to Advanced Secondary Mathematics Curriculum
This book promotes the experimental mathematics approach in the context of secondary mathematics curriculum by exploring mathematical models depending on parameters that were typically considered advanced in the pre-digital education era. This approach, by drawing on the power of computers to perform numerical computations and graphical constructions, stimulates formal learning of mathematics through making sense of a computational experiment. It allows one (in the spirit of Freudenthal) to bridge serious mathematical content and contemporary teaching practice. In other words, the notion of teaching experiment can be extended to include a true mathematical experiment. When used appropriately, the approach creates conditions for collateral learning (in the spirit of Dewey) to occur including the development of skills important for engineering applications of mathematics. In the context of a mathematics teacher education program, the book addresses a call for the preparation of teachers capable of utilizing modern technology tools for the modeling-based teaching of mathematics with a focus on methods conducive to the improvement of the whole STEM education at the secondary level. By the same token, using the book’s pedagogy and its mathematical content in a pre-college classroom can assist teachers in introducing students to the ideas that develop the foundation of engineering profession.
R 4,278.97
R 519.00
Dynamic Programming for Economists
Dynamic programming is a powerful technique for modeling problems requiring sequential decisions in macroeconomics and finance. The drawback is that its analytic foundations are mathematically demanding, making existing textbooks often too technical for beginners. Moreover, dynamic models most often cannot be solved analytically and, therefore, require the use of numerical methods, whose implementation details can be discouraging.This book is a primer on dynamic programming and related numerical techniques. To assist the reader, the discussion focuses on a specific example: a simple optimal growth model. Once the reader has mastered the most important concepts, these can be easily applied to business cycle models, New Keynesian frameworks, dynamic CAPM, and many other issues.The deterministic growth model is used to introduce dynamic programming in an intuitive way. Technicalities are reduced to a minimum, though not avoided, to provide a solid foundation for the applications (and to stimulate the interested reader toward further readings). After a discussion of cases where a closed-form solution exists, the most commonly used numerical techniques are introduced. The book considers the value function iteration method, detailing the steps needed to build the pertinent numerical routines using Matlab. The policy function iteration method and the endogenous grid approach are also discussed since—while similar in spirit to value function iteration—they offer ways to save computing time for some applications. Then, collocation methods are used to obtain a ‘global’ solution. Again, the reader is guided step-by-step in building the numerical routines. Finally, the book introduces the perturbation method, emphasizing its nature as an approximation of the ‘true’ solution.Moving in small steps, and with the aim of keeping the presentation as readable as possible, the book then focuses on stochastic versions of the growth model. The numerical techniques introduced for the deterministic version are used to deal with (persistent) productivity shocks. In addition, the book presents a method—the parameterized expectation approach—that only mildly suffers from the “curse of dimensionality”.
R 2,564.00
International Foundation Mathematics Workbook One
Students looking to build their skills through extra practice worksheets or teachers seeking already-created workbooks, reinforcing key concepts for their students, need look no further. Anyone in a foundation program or first year-college math course is bound to find this workbook approachable and immensely useful.The workbook is written by a teacher, who understands the needs of math students and has been teaching the material covered in this workbook for over five years to a diverse host of students from different backgrounds and needs. Material presented includes Linear Equations and Matrices, Quadratic and Cubic Equations, Sequences and Series, Indices and Logarithms, Trigonometry, Differentiation, and Integration. With over 2,000 level-appropriate created questions to practice, mathematical skills and confidence are sure to be reinforced and boosted.
R 977.45
Python for Algorithmic Trading
Algorithmic trading, once the exclusive domain of institutional players, is now open to small organizations and individual traders using online platforms. The tool of choice for many traders today is Python and its ecosystem of powerful packages. In this practical book, author Yves Hilpisch shows students, academics, and practitioners how to use Python in the fascinating field of algorithmic trading.You'll learn several ways to apply Python to different aspects of algorithmic trading, such as backtesting trading strategies and interacting with online trading platforms. Some of the biggest buy- and sell-side institutions make heavy use of Python. By exploring options for systematically building and deploying automated algorithmic trading strategies, this book will help you level the playing field.Set up a proper Python environment for algorithmic tradingLearn how to retrieve financial data from public and proprietary data sourcesExplore vectorization for financial analytics with NumPy and pandasMaster vectorized backtesting of different algorithmic trading strategiesGenerate market predictions by using machine learning and deep learningTackle real-time processing of streaming data with socket programming toolsImplement automated algorithmic trading strategies with the OANDA and FXCM trading platforms
R 1,824.00
C++ for Financial Mathematics
If you know a little bit about financial mathematics but don’t yet know a lot about programming, then C++ for Financial Mathematics is for you.C++ is an essential skill for many jobs in quantitative finance, but learning it can be a daunting prospect. This book gathers together everything you need to know to price derivatives in C++ without unnecessary complexities or technicalities. It leads the reader step-by-step from programming novice to writing a sophisticated and flexible financial mathematics library. At every step, each new idea is motivated and illustrated with concrete financial examples.As employers understand, there is more to programming than knowing a computer language. As well as covering the core language features of C++, this book teaches the skills needed to write truly high quality software. These include topics such as unit tests, debugging, design patterns and data structures. The book teaches everything you need to know to solve realistic financial problems in C++. It can be used for self-study or as a textbook for an advanced undergraduate or master’s level course.
R 2,976.80
Discrete Mathematics
Biggs' Discrete Mathematics has been a best-selling textbook since the first and revised editions were published in 1986 and 1990, respectively. This second edition has been developed in response to undergraduate course changes and changes in students' needs. New to this edition are chapters on statements and proof, logical framework, and natural numbers and the integers, in addition to updated chapters from the previous edition. The new chapters are presented at a level suitable for mathematics and computer science students seeking a first approach to this broad and highly relevant topic. Each chapter contains newly developed tailored exercises, and miscellaneous exercises are presented throughout, providing the student with over 1000 individualtailored exercises. This edition is accompanied by a website www.oup.com/mathematics/discretemath containing hints and solutions to all exercises presented in the text, providing an invaluable resource for students and lecturers alike. The book is carefully structured, coherent and comprehensive, and is the ideal text for students seeking a clear introduction to discrete mathematics, graph theory, combinatorics, number theory, coding theory and abstract algebra.
R 4,061.00
R 970.00
Concise Computer Mathematics
Adapted from a modular undergraduate course on computational mathematics, Concise Computer Mathematics delivers an easily accessible, self-contained introduction to the basic notions of mathematics necessary for a computer science degree. The text reflects the need to quickly introduce students from a variety of educational backgrounds to a number of essential mathematical concepts. The material is divided into four units: discrete mathematics (sets, relations, functions), logic (Boolean types, truth tables, proofs), linear algebra (vectors, matrices and graphics), and special topics (graph theory, number theory, basic elements of calculus). The chapters contain a brief theoretical presentation of the topic, followed by a selection of problems (which are direct applications of the theory) and additional supplementary problems (which may require a bit more work). Each chapter ends with answers or worked solutions for all of the problems.
R 2,717.71
R 641.00
R 6,260.26
Mathematics of Nonlinear Programming
Nonlinear programming provides an excellent opportunity to explore an interesting variety of pure and solidly applicable mathematics, numerical analysis, and computing. This text develops some of the ideas and techniques involved in the optimization methods using calculus, leading to the study of convexity. This is followed by material on basic numerical methods, least squares, the Karush-Kuhn-Tucker theorem, penalty functions, and Lagrange multipliers. The authors have aimed their presentation at the student who has a working knowledge of matrix algebra and advanced calculus, but has had no previous exposure to optimization.
R 2,496.74
R 916.00
Object-Oriented Programming via Fortran 90/95
Learn how to write technical applications in a modern object-oriented approach, using Fortran 90 or 95. This book will teach you how to stop focusing on the traditional procedural abilities of Fortran and to employ the principles of object-oriented programming to produce clear, highly efficient executable codes. Get ready now to take advantage of all the features of the finalized, fully object-oriented Fortran 200X! In addition to covering the OOP methodologies the book also covers the basic foundation of the language and good programming skills, making the book valuable also as a good migration tool for experienced Fortran programmers who want to smoothly pick up the OOP paradigm. The author highlights common themes by using comparisons with Matlab and C++ and uses numerous cross-referenced examples to convey all concepts quickly and clearly. Complete code for the examples is included on the book's web page.
R 3,622.69
Mathematics of Nonlinear Programming
Nonlinear programming provides an excellent opportunity to explore an interesting variety of pure and solidly applicable mathematics, numerical analysis, and computing. This text develops some of the ideas and techniques involved in the optimization methods using calculus, leading to the study of convexity. This is followed by material on basic numerical methods, least squares, the Karush-Kuhn-Tucker theorem, penalty functions, and Lagrange multipliers. The authors have aimed their presentation at the student who has a working knowledge of matrix algebra and advanced calculus, but has had no previous exposure to optimization.
R 2,495.85
Numerical Analysis
Students completing a year of Calculus can find novel and practical challenges in mathematics using numerical analysis. This text follows the mathematician's process of examining a problem and deciding which algebraic or calculus techniques lead to reliable solutions. Each algorithm includes all developmental steps, illustrating the beauty of reducing complex systems to streamlined formulae. Accessible proofs using techniques beyond those in geometry are fully explained. All algorithms contain detailed examples of success and situational pitfalls. An exhaustive set of practice problems was meticulously developed by hand, not AI-generated, ensuring deviations at each stage of a particular algorithm. Many exercises highlight advantages and disadvantages of algorithms through comparison.
R 2,166.00
Foundations of Python Network Programming
Foundations of Python Network Programming, Third Edition, covers all of the classic topics found in the second edition of this book, including network protocols, network data and errors, email, server architecture, and HTTP and web applications, plus updates for Python 3.Some of the new topics in this edition include:• Extensive coverage of the updated SSL support in Python 3• How to write your own asynchronous I/O loop.• An overview of the "asyncio" framework that comes with Python 3.4.• How the Flask web framework connects URLs to your Python code.• How cross-site scripting and cross-site request forgery can be used to attack your web site, and how to protect against them.• How a full-stack web framework like Django can automate the round trip from your database to the screen and back.If you're a Python programmer who needs a deep understanding of how to use Python for network-related tasks and applications, this is the book for you. From web application developers, to systems integrators, to system administrators—this book has everything that you need to know.
R 1,839.59
Python for Finance Cookbook
Use modern Python libraries such as pandas, NumPy, and scikit-learn and popular machine learning and deep learning methods to solve financial modeling problemsPurchase of the print or Kindle book includes a free eBook in the PDF formatKey FeaturesExplore unique recipes for financial data processing and analysis with PythonApply classical and machine learning approaches to financial time series analysisCalculate various technical analysis indicators and backtest trading strategiesBook DescriptionPython is one of the most popular programming languages in the financial industry, with a huge collection of accompanying libraries. In this new edition of the Python for Finance Cookbook, you will explore classical quantitative finance approaches to data modeling, such as GARCH, CAPM, factor models, as well as modern machine learning and deep learning solutions.You will use popular Python libraries that, in a few lines of code, provide the means to quickly process, analyze, and draw conclusions from financial data. In this new edition, more emphasis was put on exploratory data analysis to help you visualize and better understand financial data. While doing so, you will also learn how to use Streamlit to create elegant, interactive web applications to present the results of technical analyses.Using the recipes in this book, you will become proficient in financial data analysis, be it for personal or professional projects. You will also understand which potential issues to expect with such analyses and, more importantly, how to overcome them.What you will learnPreprocess, analyze, and visualize financial dataExplore time series modeling with statistical (exponential smoothing, ARIMA) and machine learning modelsUncover advanced time series forecasting algorithms such as Meta's ProphetUse Monte Carlo simulations for derivatives valuation and risk assessmentExplore volatility modeling using univariate and multivariate GARCH modelsInvestigate various approaches to asset allocationLearn how to approach ML-projects using an example of default predictionExplore modern deep learning models such as Google's TabNet, Amazon's DeepAR and NeuralProphetWho this book is forThis book is intended for financial analysts, data analysts and scientists, and Python developers with a familiarity with financial concepts. You'll learn how to correctly use advanced approaches for analysis, avoid potential pitfalls and common mistakes, and reach correct conclusions for a broad range of finance problems.Working knowledge of the Python programming language (particularly libraries such as pandas and NumPy) is necessary.
R 1,628.00
R 611.00
Optimizing Visual Studio Code for Python Development
Learn Visual Studio Code and implement its features in Python coding, debugging, linting, and overall project management. This book addresses custom scenarios for writing programs in Python frameworks, such as Django and Flask.The book starts with an introduction to Visual Studio Code followed by code editing in Python. Here, you will learn about the required extensions of Visual Studio Code to perform various functions such as linting and debugging in Python. Next, you will set up the environment and run your projects along with the support for Jupyter. You will also work with Python frameworks such as Django and go through data science specific-information and tutorials. Finally, you will learn how to integrate Azure for Python and how to use containers in Visual Studio Code.Optimizing Visual Studio Code for Python Development is your ticket to writing Python scripts with this versatile code editor.What You'll LearnExecute Flask development in Visual Studio Code for control over libraries used in an applicationOptimize Visual Studio Code to code faster and betterUnderstand linting and debugging Python code in Visual Studio CodeWork with Jupyter Notebooks in Visual Studio CodeWho This Book Is ForPython developers, beginners, and experts looking to master Visual Studio Code
R 1,544.76
