Search results for "Python Programming for Mathematics"
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R 580.00
R 397.00
Practical Numerical Mathematics With Matlab: Solutions
This workbook and solutions manual is intended for advanced undergraduate or beginning graduate students as a supplement to a traditional course in numerical mathematics and as preparation for independent research involving numerical mathematics. The solutions manual provides complete MATLAB code and numerical results for each of the exercises in the workbook and will be especially useful for those students without previous MATLAB programming experience. It is also valuable for classroom instructors to help pinpoint the author's intent in each exercise and to provide a model for graders. Upon completion of this material, students will have a working knowledge of MATLAB programming, they will have themselves programmed algorithms encountered in classwork and textbooks, and they will know how to check and verify their own programs against hand calculations and by reference to theoretical results, special polynomial solutions and other specialized solutions. No previous programming experience with MATLAB is necessary.
R 2,287.80
R 519.00
Python for Finance Cookbook
Solve common and not-so-common financial problems using Python libraries such as NumPy, SciPy, and pandasKey FeaturesUse powerful Python libraries such as pandas, NumPy, and SciPy to analyze your financial dataExplore unique recipes for financial data analysis and processing with PythonEstimate popular financial models such as CAPM and GARCH using a problem-solution approachBook DescriptionPython is one of the most popular programming languages used in the financial industry, with a huge set of accompanying libraries.In this book, you'll cover different ways of downloading financial data and preparing it for modeling. You'll calculate popular indicators used in technical analysis, such as Bollinger Bands, MACD, RSI, and backtest automatic trading strategies. Next, you'll cover time series analysis and models, such as exponential smoothing, ARIMA, and GARCH (including multivariate specifications), before exploring the popular CAPM and the Fama-French three-factor model. You'll then discover how to optimize asset allocation and use Monte Carlo simulations for tasks such as calculating the price of American options and estimating the Value at Risk (VaR). In later chapters, you'll work through an entire data science project in the financial domain. You'll also learn how to solve the credit card fraud and default problems using advanced classifiers such as random forest, XGBoost, LightGBM, and stacked models. You'll then be able to tune the hyperparameters of the models and handle class imbalance. Finally, you'll focus on learning how to use deep learning (PyTorch) for approaching financial tasks.By the end of this book, you’ll have learned how to effectively analyze financial data using a recipe-based approach.What you will learnDownload and preprocess financial data from different sourcesBacktest the performance of automatic trading strategies in a real-world settingEstimate financial econometrics models in Python and interpret their resultsUse Monte Carlo simulations for a variety of tasks such as derivatives valuation and risk assessmentImprove the performance of financial models with the latest Python librariesApply machine learning and deep learning techniques to solve different financial problemsUnderstand the different approaches used to model financial time series dataWho this book is forThis book is for financial analysts, data analysts, and Python developers who want to learn how to implement a broad range of tasks in the finance domain. Data scientists looking to devise intelligent financial strategies to perform efficient financial analysis will also find this book useful. Working knowledge of the Python programming language is mandatory to grasp the concepts covered in the book effectively.
R 1,498.00
Oxford IB Diploma Programme: IB Mathematics: applications and interpretation, Standard Level, Print and Enhanced Online Course Book Pack
Featuring a wealth of digital content, this concept-based Print and Enhanced Online Course Book Pack has been developed in cooperation with the IB to provide the most comprehensive support for the new DP Mathematics: applications and interpretation SL syllabus, for first teaching in September 2019. Each Enhanced Online Course Book Pack is made up of one full-colour, print textbook and one online textbook - packed full of investigations, exercises, worksheets, workedsolutions and answers, plus assessment preparation support.
R 4,170.00
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 2,506.00
Numerical Mathematics
This book provides the mathematical foundations of numerical methods and demonstrates their performance on examples, exercises and real-life applications. This is done using the MATLAB software environment, which allows an easy implementation and testing of the algorithms for any specific class of problems. The book is addressed to students in Engineering, Mathematics, Physics and Computer Sciences. In the second edition of this extremely popular textbook on numerical analysis, the readability of pictures, tables and program headings has been improved. Several changes in the chapters on iterative methods and on polynomial approximation have also been
R 3,367.09
R 244.00
Mathematics Before and After Pythagoras
This book provides the reader with a comprehensive account of the contributions of Pythagoras to mathematics and philosophy, using them as a starting point to compare pre-Pythagorean accomplishments with the myriad mathematical developments that followed. It begins with a thorough study of Pythagoreanism and the early Pythagoreans, including the major events in Pythagoras' life and the origins of the mystical significance attributed by Pythagoreans to natural numbers. From Chapter 3 onward, the book describes how mathematical thinking works and prepares the reader for the subsequent chapters, which cover mathematical logic and proofs, their application to the study of natural and prime numbers, the investigation of Pythagorean triples, figurative numbers, and irrational numbers, all interwoven with rich historical context.Aimed at students and teachers at all levels, this work is accessible to non-mathematicians as well, with the main prerequisite being an avid curiosity about some of the ideas and thinkers that helped to forge the mathematical world as we know it. Early praises for “Mathematics Before and After Pythagoras”:“Your book is charming and fun to read. It would be fine to be able to teach from it.” (Steve Krantz, USA)“...your new book, an obvious labor of love... I can see that it will be an inspiration for young students.” (Bruce Berndt, USA)“It is an excellent book, and I am deeply grateful for sending it to me. It is an extraordinary gift, and I am so grateful for this.” (Carlo Cattani, Italy)“I am really impressed by the wealth of interesting material you have collected and presented.” (Rainer Kress, Germany)
R 7,769.61
Stair-Step Approach in Mathematics
This book is intended as a teacher’s manual and as an independent-study handbook for students and mathematical competitors. Based on a traditional teaching philosophy and a non-traditional writing approach (the stair-step method), this book consists of new problems with solutions created by the authors. The main idea of this approach is to start from relatively easy problems and “step-by-step” increase the level of difficulty toward effectively maximizing students' learning potential. In addition to providing solutions, a separate table of answers is also given at the end of the book. A broad view of mathematics is covered, well beyond the typical elementary level, by providing more in depth treatment of Geometry and Trigonometry, Number Theory, Algebra, Calculus, and Combinatorics.
R 2,381.00
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 9,308.07
Intermediate Python and Large Language Models
Harness the power of Large Language Models (LLMs) to build cutting-edge AI applications with Python and LangChain. This book provides a hands-on approach to understanding, implementing, and deploying LLM-powered solutions, equipping developers, data scientists, and AI enthusiasts with the tools to create real-world AI applications. The journey begins with an introduction to LangChain, covering its core concepts, integration with Python, and essential components such as prompt engineering, memory management, and retrieval-augmented generation (RAG). As you progress, you'll explore advanced AI workflows, including multi-agent architectures, fine-tuning strategies, and optimization techniques to maximize LLM efficiency. The book also takes a deep dive into practical applications of LLMs, guiding you through the development of intelligent chatbots, document retrieval systems, content generation pipelines, and AI-driven automation tools. You'll learn how to leverage APIs, integrate LLMs into web and mobile platforms, and optimize large-scale deployments while addressing key challenges such as inference latency, cost efficiency, and ethical considerations. By the end of the book, you'll have gained a solid understanding of LLM architectures, hands-on experience with LangChain, and the expertise to build scalable AI applications that redefine human-computer interaction. What You Will Learn Understand the fundamentals of LangChain and Python for LLM development Know advanced AI workflows, including fine-tuning and memory management Build AI-powered applications such as chatbots, retrieval systems, and automation tools Know deployment strategies and performance optimization for real-world use Use best practices for scalability, security, and responsible AI implementation Unlock the full potential of LLMs and take your AI development skills to the next level Who This Book Is For Software engineers and Python developers interested in learning the foundations of LLMs and building advanced modern LLM applications for various tasks
R 1,254.00
Beginnings of Greek Mathematics
When this book was first published, more than five years ago, I added an appendix on How the Pythagoreans discovered Proposition 11.5 of the 'Elements'. I hoped that this appendix, although different in some ways from the rest of the book, would serve to illustrate the kind of research which needs to be undertaken, if we are to acquire a new understanding of the historical development of Greek mathematics. It should perhaps be mentioned that this book is not intended to be an introduction to Greek mathematics for the general reader; its aim is to bring the problems associated with the early history of deductive science to the attention of classical scholars, and historians and philos ophers of science. I should like to conclude by thanking my translator, Mr. A. M. Ungar, who worked hard to produce something more than a mechanical translation. Much of his work was carried out during the year which I spent at Stanford as a fellow of the Center for Advanced Study in the Behavioral Sciences. This enabled me to supervise the work of transla tion as it progressed. I am happy to express my gratitude to the Center for providing me with this opportunity. Arpad Szabo NOTE ON REFERENCES The following books are frequently referred to in the notes. Unless otherwise stated, the editions are those given below. Burkert, W. Weisheit und Wissensclzaft, Studien zu Pythagoras, Philo laos und Platon, Nuremberg 1962.
R 6,011.86
Mathematics of Program Construction
This book constitutes the refereed proceedings of the 11th International Conference on Mathematics of Program Construction, MPC 2012, held in Madrid, Spain, in June 2012. The 13 revised full papers presented together with three invited talks were carefully reviewed and selected from 27 submissions. The papers are organized in topical sections on security and information flow, synchronous and real-time systems, algorithms and games, program calculi, tool support, algebras and datatypes, and categorical functional programming.
R 1,568.42
Essential Programming for the Technical Artist
This book is based on a successful curriculum designed to elevate technical artists with no programming experience up to essential programming competency as quickly as possible. Instead of abstract, theoretical problems, the curriculum employs familiar applications encountered in real production environments to demonstrate each lesson.Written with artists in mind, this book introduces novice programmers to the advantageous world of Python programming with relevant and familiar examples. Any digital artists (not just technical artists) will find this book helpful in assisting with day-to-day production activities.Concentrating upon subjects relevant to the creation of computer graphic assets, this book introduces Python basics, functions, data types, object-oriented programming, exception handling, file processing, graphical user interface creation, PEP 8 standards, and regular expressions. Programming within the SideFX Houdini 3D animation software provides a familiar environment for artists to create and experiment with the covered Python topics.
R 3,260.00
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
Introduction to Discrete Mathematics with ISETL
Intended for first- or second-year undergraduates, this introduction to discrete mathematics covers the usual topics of such a course, but applies constructivist principles that promote - indeed, require - active participation by the student. Working with the programming language ISETL, whose syntax is close to that of standard mathematical language, the student constructs the concepts in her or his mind as a result of constructing them on the computer in the syntax of ISETL. This dramatically different approach allows students to attempt to discover concepts in a "Socratic" dialog with the computer. The discussion avoids the formal "definition-theorem" approach and promotes active involvement by the reader by its questioning style. An instructor using this text can expect a lively class whose students develop a deep conceptual understanding rather than simply manipulative skills. Topics covered in this book include: the propositional calculus, operations on sets, basic counting methods, predicate calculus, relations, graphs, functions, and mathematical induction.
R 2,159.79
Python for Information Professionals
Python for Information Professionals: How to Design Practical Applications to Capitalize on the Data Explosion is an introduction to the Python programming language for library and information professionals with little or no prior experience. As opposed to the many Python books available today that focus on the language only from a general sense, this book is designed specifically for information professionals who are seeking to advance their career prospects or challenge themselves in new ways by acquiring skills within the rapidly expanding field of data science. Readers of Python for Information Professionals will learn to:Develop Python applications for the retrieval, cleaning, and analysis of large datasets. Design applications to support traditional library functions and create new opportunities to maximize library value. Consider data security and privacy relevant to data analysis when using the Python language.
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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
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
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
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
Computational and Analytical Mathematics
The research of Jonathan Borwein has had a profound impact on optimization, functional analysis, operations research, mathematical programming, number theory, and experimental mathematics. Having authored more than a dozen books and more than 300 publications, Jonathan Borwein is one of the most productive Canadian mathematicians ever. His research spans pure, applied, and computational mathematics as well as high performance computing, and continues to have an enormous impact: MathSciNet lists more than 2500 citations by more than 1250 authors, and Borwein is one of the 250 most cited mathematicians of the period 1980-1999. He has served the Canadian Mathematics Community through his presidency (2000–02) as well as his 15 years of editing the CMS book series.Jonathan Borwein’s vision and initiative have been crucial in initiating and developing several institutions that provide support for researchers with a wide range of scientific interests. A few notable examples include the Centre for Experimental and Constructive Mathematics and the IRMACS Centre at Simon Fraser University, the Dalhousie Distributed Research Institute at Dalhousie University, the Western Canada Research Grid, and the Centre for Computer Assisted Research Mathematics and its Applications, University of Newcastle.The workshops that were held over the years in Dr. Borwein’s honor attracted high-caliber scientists from a wide range of mathematical fields. This present volume is an outgrowth of the workshop on ‘Computational and Analytical Mathematics’ held in May 2011 in celebration of Dr. Borwein’s 60th Birthday. The collection contains various state-of-the-art research manuscripts and surveys presenting contributions that have risen from the conference, and is an excellent opportunity to survey state-of-the-art research and discuss promising research directions and approaches.
R 6,228.00
Applied Mathematics: Body and Soul
Applied Mathematics: Body & Soul is a mathematics education reform project developed at Chalmers University of Technology and includes a series of volumes and software. The program is motivated by the computer revolution opening new possibilities of computational mathematical modeling in mathematics, science and engineering. It consists of a synthesis of Mathematical Analysis (Soul), Numerical Computation (Body) and Application. Volumes I-III present a modern version of Calculus and Linear Algebra, including constructive/numerical techniques and applications intended for undergraduate programs in engineering and science. Further volumes present topics such as Dynamical Systems, Fluid Dynamics, Solid Mechanics and Electro-Magnetics on an advanced undergraduate/graduate level. The authors are leading researchers in Computational Mathematics who have written various successful books.
R 2,190.33
Mathematics for Computer Scientists
This textbook contains the mathematics needed to study computer science in application-oriented computer science courses. The content is based on the author's many years of teaching experience.The translation of the original German 7th edition Mathematik für Informatiker by Peter Hartmann was done with the help of artificial intelligence. A subsequent human revision was done primarily in terms of content.Textbook FeaturesYou will always find applications to computer science in this book.Not only will you learn mathematical methods, you will gain insights into the ways of mathematical thinking to form a foundation for understanding computer science.Proofs are given when they help you learn something, not for the sake of proving.Mathematics is initially a necessary evil for many students. The author explains in each lesson how students can apply what they have learned by giving many real world examples, and by constantly cross-referencing math and computer science. Students will see how math is not only useful, but can be interesting and sometimes fun.The ContentSets, logic, number theory, algebraic structures, cryptography, vector spaces, matrices, linear equations and mappings, eigenvalues, graph theory.Sequences and series, continuous functions, differential and integral calculus, differential equations, numerics.Probability theory and statistics.The Target AudiencesStudents in all computer science-related coursework, and independent learners.
R 3,573.48
R 916.00
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 611.00
Numerical Mathematics
This book provides the mathematical foundations of numerical methods and demonstrates their performance on examples, exercises and real-life applications. This is done using the MATLAB software environment, which allows an easy implementation and testing of the algorithms for any specific class of problems. The book is addressed to students in Engineering, Mathematics, Physics and Computer Sciences. In the second edition of this extremely popular textbook on numerical analysis, the readability of pictures, tables and program headings has been improved. Several changes in the chapters on iterative methods and on polynomial approximation have also been
R 2,829.20
R 610.00
R 1,221.00
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
R 658.00
Machine Learning with Python
This textbook presents an in-depth coverage of machine learning and deep learning using Python programming. It introduces concepts, algorithms, and methodologies for understanding the basic components of machine learning, including supervised, unsupervised and reinforcement learning.
R 2,257.00
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
