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Numerical Python
Learn how to leverage the scientific computing and data analysis capabilities of Python, its standard library, and popular open-source numerical Python packages like NumPy, SymPy, SciPy, matplotlib, and more. This book demonstrates how to work with mathematical modeling and solve problems with numerical, symbolic, and visualization techniques. It explores applications in science, engineering, data analytics, and more.Numerical Python, Third Edition, presents many case study examples of applications in fundamental scientific computing disciplines, as well as in data science and statistics. This fully revised edition, updated for each library's latest version, demonstrates Python's power for rapid development and exploratory computing due to its simple and high-level syntax and many powerful libraries and tools for computation and data analysis. After reading this book, readers will be familiar with many computing techniques, including array-based and symbolic computing, visualization and numerical file I/O, equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling, and machine learning.What You'll LearnWork with vectors and matrices using NumPyReview Symbolic computing with SymPyPlot and visualize data with MatplotlibPerform data analysis tasks with Pandas and SciPyUnderstand statistical modeling and machine learning with statsmodels and scikit-learnOptimize Python code using Numba and CythonWho This Book Is ForDevelopers who want to understand how to use Python and its ecosystem of libraries for scientific computing and data analysis.
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Python for Engineers and Scientists
This book equips technical professionals with a complete, structured path to mastering Python for real-world engineering and scientific applications. As Python becomes central to research labs, automation, simulation, and data-driven engineering, the learning curve remains steep. This book bridges that gap with a practical roadmap from fundamentals to professional-grade scientific computing.The book begins by building a strong programming foundation—covering Python’s architecture, essential syntax, data structures, modules, functions, control flow, and object-oriented design. Readers learn not only how to write Python, but why the language behaves the way it does, ensuring deep understanding and strong problem-solving skills. From there, the focus shifts to practical engineering tools. Dedicated chapters train readers in file handling, math modules, and key scientific libraries including NumPy, SciPy, Pandas, and Matplotlib, enabling efficient computation, signal processing, data analysis, and high-quality visualization. Advanced chapters then showcase Python’s versatility through GUI development with Kivy, database work with SQLAlchemy, and hands-on control system modeling and analysis. Each topic is rooted in industry and academic scenarios, making the content directly applicable to automation, simulation, data workflows, and engineering computation.Whether you're a student building foundational skills or a professional modernizing engineering workflows, this guide provides the programming depth, scientific tools, and applied knowledge needed to use Python confidently for prototyping, simulation, analysis, and automation. It is the one-stop resource for mastering Python in technical disciplines.What you will learn:Learn core Python programming concepts, including variables, loops, functions, and object-oriented principles in a scientific and engineering context.Build strong skills in key scientific Python libraries such as NumPy, Pandas, Matplotlib, and SciPy for computation, analysis, and visualization.Apply Python to real-world engineering scenarios, including dynamic control systems and automation workflows.Develop interactive engineering applications by creating graphical user interfaces (GUIs) with Kivy. Who this book is for:This book is intended for practicing engineers and scientists across various disciplines of engineering who need to use Python for complex problem-solving, simulation, and data analysis in their work.
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Architecture of Advanced Numerical Analysis Systems
PrologueA Brief HistoryReductionism vs. HolismKey FeaturesContact MePART 1: NUMERICAL TECHNIQUES1. IntroductionWhat Is Scientific ComputingWhat is Functional ProgrammingWho Is This Book ForStructure of the BookInstallationOption 1: Install from OPAMOption 2: Pull from Docker HubOption 3: Pin the Dev-RepoOption 4: Compile from SourceCBLAS/LAPACKE DependencyInteracting with OwlUsing ToplevelUsing NotebookUsing Owl-JupyterSummary2. ConventionsPure vs. ImpureNdarray vs. ScalarInfix OperatorsOperator ExtensionModule StructuresNumber and PrecisionPolymorphic FunctionsModule ShortcutsType Casting3. VisualisationCreate PlotsSpecificationSubplotsMultiple LinesLegendDrawing PatternsLine PlotScatter PlotStairs PlotBox PlotStem PlotArea PlotHistogram & CDF PlotLog Plot3D PlotAdvanced Statistical PlotSummaryReferences4. Mathematical FunctionsBasic FunctionsBasic Unary Math FunctionsBasic Binary FunctionsExponential and Logarithmic FunctionsTrigonometric FunctionsOther Math FunctionsSpecial FunctionsAiry FunctionsBessel FunctionsElliptic FunctionsGamma FunctionsBeta FunctionsStruve FunctionsZeta FunctionsError FunctionsIntegral FunctionsFactorialsInterpolation and ExtrapolationIntegrationUtility FunctionsSummary5. Statistical FunctionsRandom VariablesDiscrete Random VariablesContinuous Random VariablesDescriptive StatisticsOrder StatisticsSpecial DistributionGamma DistributionBeta DistributionChi-Square DistributionStudent-t DistributionCauchy DistributionMultiple VariablesSamplingHypothesis TestsTheoryGaussian Distribution in Hypothesis TestingTwo-Sample InferencesGoodness-of-fit TestsNon-parametric StatisticsCovariance and CorrelationsAnalysis of VarianceSummary6. N-Dimensional ArraysNdarray TypesCreation FunctionsProperties FunctionsMap FunctionsFold FunctionsScan FunctionsComparison FunctionsVectorised FunctionsIteration FunctionsManipulation FunctionsSerialisationTensorsSummaryReferences7. Slicing and BroadcastingSlicingBasic SlicingFancy SlicingConventions in DefinitionExtended OperatorsAdvanced UsageBroadcastingWhat Is Broadcasting?Shape ConstraintsSupported OperationsSlicing in NumPy and JuliaInternal MechanismSummary8. Linear AlgebraVectors and MatricesCreating MatricesAccessing ElementsIterate, Map, Fold, and FilterMath OperationsGaussian EliminationLU FactorisationInverse and TransposeVector SpacesRank and BasisOrthogonalitySolving Ax = bMatrix SensitivityDeterminantsEigenvalues and EigenvectorsSolving
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Python for AI, Machine Learning, and Deep Learning
Learn AI, machine learning, and deep learning with Python from the ground up, then build and tune models with XGBoost, TensorFlow, PyTorch, CNNs, RNNs, transformers, and transfer learning through practical Python projects.Key FeaturesBuild Python, data science, mathematics, and statistics foundations for machine learningTrain, evaluate, and tune regression, classification, ensemble, XGBoost, LightGBM, and CatBoost modelsCreate CNN, RNN, transformer, and transfer learning projects with TensorFlow, Keras, and PyTorchBook DescriptionLearning modern AI often feels fragmented: Python in one resource, mathematics in another, and deep learning or transformers somewhere else. This book brings those foundations into one practical progression, helping you connect the concepts behind machine learning with the code used to build models.You will start with Python programming, NumPy, Pandas, data visualization, exploratory data analysis, linear algebra, calculus, probability, and statistics. From there, you will build supervised learning skills across regression, classification, k-NN, feature engineering, model evaluation, cross-validation, and hyperparameter tuning. You will then work with ensemble methods including random forests, gradient boosting, XGBoost, LightGBM, and CatBoost, while learning to handle imbalanced data and justify modeling decisions.The final part moves into neural networks and deep learning with TensorFlow, Keras, and PyTorch. You will build CNNs for image classification, explore RNNs, LSTMs, GRUs, word embeddings, and sequence-to-sequence models, and understand attention and transformer architectures through BERT and GPT examples. You will finish by applying transfer learning and fine-tuning to computer vision and NLP tasks, supported by projects that turn theory into practical AI and machine learning skills for real-world problem solving.What you will learnUse Python, NumPy, Pandas, Matplotlib, and Seaborn for data analysisApply linear algebra, calculus, probability, and statistics to MLBuild regression, classification, and k-NN modelsEngineer features and evaluate models with cross-validation and hyperparameter tuningTrain ensembles with random forests, XGBoost, LightGBM, and CatBoostBuild neural networks and CNNs with TensorFlow, Keras, and PyTorchDevelop RNNs, LSTMs, GRUs, attention, BERT, and GPT workflows for NLPApply transfer learning and fine-tuning to computer vision and NLPWho this book is forThis book is for aspiring data scientists, machine learning engineers, AI developers, students, and career changers who want one structured path from Python fundamentals to machine learning and deep learning. It suits readers seeking practical skills in data analysis, predictive modeling, computer vision, NLP, transformers, and transfer learning. No prior machine learning or deep learning experience is required. Python is introduced from the fundamentals, although basic programming familiarity and foundational mathematics will help.
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Numerical Methods
The fourth edition of Numerical Methods Using MATLAB® provides a clear and rigorous introduction to a wide range of numerical methods that have practical applications. The authors’ approach is to integrate MATLAB® with numerical analysis in a way which adds clarity to the numerical analysis and develops familiarity with MATLAB®. MATLAB® graphics and numerical output are used extensively to clarify complex problems and give a deeper understanding of their nature. The text provides an extensive reference providing numerous useful and important numerical algorithms that are implemented in MATLAB® to help researchers analyze a particular outcome. By using MATLAB® it is possible for the readers to tackle some large and difficult problems and deepen and consolidate their understanding of problem solving using numerical methods. Many worked examples are given together with exercises and solutions to illustrate how numerical methods can be used to study problems that have applications in the biosciences, chaos, optimization and many other fields. The text will be a valuable aid to people working in a wide range of fields, such as engineering, science and economics.
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Introduction to Numerical Analysis
Numerical analysis is an increasingly important link between pure mathematics and its application in science and technology. This textbook provides an introduction to the justification and development of constructive methods that provide sufficiently accurate approximations to the solution of numerical problems, and the analysis of the influence that errors in data, finite-precision calculations, and approximation formulas have on results, problem formulation and the choice of method. It also serves as an introduction to scientific programming in MATLAB, including many simple and difficult, theoretical and computational exercises. A unique feature of this book is the consequent development of interval analysis as a tool for rigorous computation and computer assisted proofs, along with the traditional material.
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Introduction to Numerical Analysis
Numerical analysis is an increasingly important link between pure mathematics and its application in science and technology. This textbook provides an introduction to the justification and development of constructive methods that provide sufficiently accurate approximations to the solution of numerical problems, and the analysis of the influence that errors in data, finite-precision calculations, and approximation formulas have on results, problem formulation and the choice of method. It also serves as an introduction to scientific programming in MATLAB, including many simple and difficult, theoretical and computational exercises. A unique feature of this book is the consequent development of interval analysis as a tool for rigorous computation and computer assisted proofs, along with the traditional material.
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Numerical Methods in Physics with Python
Bringing together idiomatic Python programming, foundational numerical methods, and physics applications, this is an ideal standalone textbook for courses on computational physics. All the frequently used numerical methods in physics are explained, including foundational techniques and hidden gems on topics such as linear algebra, differential equations, root-finding, interpolation, and integration. The second edition of this introductory book features several new codes and 140 new problems (many on physics applications), as well as new sections on the singular-value decomposition, derivative-free optimization, Bayesian linear regression, neural networks, and partial differential equations. The last section in each chapter is an in-depth project, tackling physics problems that cannot be solved without the use of a computer. Written primarily for students studying computational physics, this textbook brings the non-specialist quickly up to speed with Python before looking in detail at the numerical methods often used in the subject.
R 5,259.00
Numerical Methods in Physics with Python
Bringing together idiomatic Python programming, foundational numerical methods, and physics applications, this is an ideal standalone textbook for courses on computational physics. All the frequently used numerical methods in physics are explained, including foundational techniques and hidden gems on topics such as linear algebra, differential equations, root-finding, interpolation, and integration. The second edition of this introductory book features several new codes and 140 new problems (many on physics applications), as well as new sections on the singular-value decomposition, derivative-free optimization, Bayesian linear regression, neural networks, and partial differential equations. The last section in each chapter is an in-depth project, tackling physics problems that cannot be solved without the use of a computer. Written primarily for students studying computational physics, this textbook brings the non-specialist quickly up to speed with Python before looking in detail at the numerical methods often used in the subject.
R 2,353.00
Fundamentals of Numerical Computing
This book examines the solution of some of the most common problems of numerical computation. By concentrating on one effective algorithm for each basic task, it develops the fundamental theory in a brief, elementary way. There are ample exercises, and codes are provided to reduce the time otherwise required for programming and debugging. Exposes reader to the art of numerical computing as well as the science.
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Numerical Linear Algebra
This self-contained introduction to numerical linear algebra provides a comprehensive, yet concise, overview of the subject. It includes standard material such as direct methods for solving linear systems and least-squares problems, error, stability and conditioning, basic iterative methods and the calculation of eigenvalues. Later chapters cover more advanced material, such as Krylov subspace methods, multigrid methods, domain decomposition methods, multipole expansions, hierarchical matrices and compressed sensing. The book provides rigorous mathematical proofs throughout, and gives algorithms in general-purpose language-independent form. Requiring only a solid knowledge in linear algebra and basic analysis, this book will be useful for applied mathematicians, engineers, computer scientists, and all those interested in efficiently solving linear problems.
R 2,105.88
Numerical Linear Algebra
This self-contained introduction to numerical linear algebra provides a comprehensive, yet concise, overview of the subject. It includes standard material such as direct methods for solving linear systems and least-squares problems, error, stability and conditioning, basic iterative methods and the calculation of eigenvalues. Later chapters cover more advanced material, such as Krylov subspace methods, multigrid methods, domain decomposition methods, multipole expansions, hierarchical matrices and compressed sensing. The book provides rigorous mathematical proofs throughout, and gives algorithms in general-purpose language-independent form. Requiring only a solid knowledge in linear algebra and basic analysis, this book will be useful for applied mathematicians, engineers, computer scientists, and all those interested in efficiently solving linear problems.
R 4,290.00
Elements of Numerical Analysis
Numerical analysis deals with the manipulation of numbers to solve a particular problem. This book discusses in detail the creation, analysis and implementation of algorithms to solve the problems of continuous mathematics. An input is provided in the form of numerical data or it is generated as required by the system to solve a mathematical problem. Subsequently, this input is processed through arithmetic operations together with logical operations in a systematic manner and an output is produced in the form of numbers. Covering the fundamentals of numerical analysis and its applications in one volume, this book offers detailed discussion on relevant topics including difference equations, Fourier series, discrete Fourier transforms and finite element methods. In addition, the important concepts of integral equations, Chebyshev Approximation and Eigen Values of Symmetric Matrices are elaborated upon in separate chapters. The book will serve as a suitable textbook for undergraduate students in science and engineering.
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Fundamentals of Numerical Mathematics for Physicists and Engineers
Introduces the fundamentals of numerical mathematics and illustrates its applications to a wide variety of disciplines in physics and engineering Applying numerical mathematics to solve scientific problems, this book helps readers understand the mathematical and algorithmic elements that lie beneath numerical and computational methodologies in order to determine the suitability of certain techniques for solving a given problem. It also contains examples related to problems arising in classical mechanics, thermodynamics, electricity, and quantum physics. Fundamentals of Numerical Mathematics for Physicists and Engineers is presented in two parts. Part I addresses the root finding of univariate transcendental equations, polynomial interpolation, numerical differentiation, and numerical integration. Part II examines slightly more advanced topics such as introductory numerical linear algebra, parameter dependent systems of nonlinear equations, numerical Fourier analysis, and ordinary differential equations (initial value problems and univariate boundary value problems). Chapters cover: Newton’s method, Lebesgue constants, conditioning, barycentric interpolatory formula, Clenshaw-Curtis quadrature, GMRES matrix-free Krylov linear solvers, homotopy (numerical continuation), differentiation matrices for boundary value problems, Runge-Kutta and linear multistep formulas for initial value problems. Each section concludes with Matlab hands-on computer practicals and problem and exercise sets. This book: Provides a modern perspective of numerical mathematics by introducing top-notch techniques currently used by numerical analysts Contains two parts, each of which has been designed as a one-semester courseIncludes computational practicals in Matlab (with solutions) at the end of each section for the instructor to monitor the student's progress through potential exams or short projectsContains problem and exercise sets (also with solutions) at the end of each section Fundamentals of Numerical Mathematics for Physicists and Engineers is an excellent book for advanced undergraduate or graduate students in physics, mathematics, or engineering. It will also benefit students in other scientific fields in which numerical methods may be required such as chemistry or biology.
R 4,030.00
Numerical Methods
Numerical Methods Using MATLAB®, Fifth Edition continues to provide a clear and rigorous introduction to a wide range of numerical methods that have practical applications. The authors’ approach is to integrate MATLAB with numerical analysis in a way which adds clarity to the numerical analysis and develops familiarity with MATLAB. MATLAB graphics and numerical output are used extensively to clarify complex problems and give a deeper understanding of their nature. The text provides an extensive reference providing numerous useful and important numerical algorithms that are implemented in MATLAB to help researchers analyze a particular outcome.By using MATLAB it is possible for the readers to tackle some large and difficult problems and deepen and consolidate their understanding of problem solving using numerical methods. Many worked examples are given together with exercises and solutions to illustrate how numerical methods can be used to study problems that have applications in the biosciences, chaos, optimization and many other fields. The text will be a valuable aid to people working in a wide range of fields, such as engineering, science and economics.
R 3,531.00
Numerical Mathematics
Numerical mathematics is one of the cornerstones of the study of mathematics, engineering, physics and computer science. This two-volume textbook is designed for introductory lectures and lays a solid foundation for further learning units. The first volume covers the entire field of numerical mathematics from classical techniques such as the Gaussian algorithm and Newton’s method to modern algorithms such as least squares approximation using Householder and Givens transformations and deflation techniques. The methods are described with mathematical precision, implemented in MATLAB codes and demonstrated using examples. The MATLAB codes are available for download on the publisher’s website so that readers can carry out their own experiments with the numerical methods. Thanks to its didactic structure and the numerous illustrative examples and exercises, this book is ideal for reading alongside lectures and as a basis for successful self-study. At the same time, it can be used by mathematicians, scientists and engineers as a profound reference work.
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Numerical Methods for Engineers and Scientists
Numerical Methods for Engineers and Scientists, 3rd Edition provides engineers with a more concise treatment of the essential topics of numerical methods while emphasizing MATLAB use. The third edition includes a new chapter, with all new content, on Fourier Transform and a new chapter on Eigenvalues (compiled from existing Second Edition content). The focus is placed on the use of anonymous functions instead of inline functions and the uses of subfunctions and nested functions. This updated edition includes 50% new or updated Homework Problems, updated examples, helping engineers test their understanding and reinforce key concepts.
R 5,766.00
Numerical Linear Algebra with Applications
Numerical Linear Algebra with Applications: Using MATLAB and Octave, Second Edition provides practical knowledge on modern computational techniques for the numerical solution of linear algebra problems. The book offers a unified presentation of computation, basic algorithm analysis, and numerical methods to compute solutions. Useful to readers regardless of background, the text begins with six introductory courses to provide background for those who haven’t taken applied or theoretical linear algebra. This approach offers a thorough explanation of the issues and methods for practical computing using MATLAB as the vehicle for computation.Appropriate for advanced undergraduate and early graduate courses on numerical linear algebra, this useful textbook explores numerous applications to engineering and science.
R 3,825.00
Practical Numerical and Scientific Computing with MATLAB® and Python
Practical Numerical and Scientific Computing with MATLAB® and Python concentrates on the practical aspects of numerical analysis and linear and non-linear programming. It discusses the methods for solving different types of mathematical problems using MATLAB and Python. Although the book focuses on the approximation problem rather than on error analysis of mathematical problems, it provides practical ways to calculate errors.The book is divided into three parts, covering topics in numerical linear algebra, methods of interpolation, numerical differentiation and integration, solutions of differential equations, linear and non-linear programming problems, and optimal control problems.This book has the following advantages:It adopts the programming languages, MATLAB and Python, which are widely used among academics, scientists, and engineers, for ease of use and contain many libraries covering many scientific and engineering fields.It contains topics that are rarely found in other numerical analysis books, such as ill-conditioned linear systems and methods of regularization to stabilize their solutions, nonstandard finite differences methods for solutions of ordinary differential equations, and the computations of the optimal controls. It provides a practical explanation of how to apply these topics using MATLAB and Python.It discusses software libraries to solve mathematical problems, such as software Gekko, pulp, and pyomo. These libraries use Python for solutions to differential equations and static and dynamic optimization problems.Most programs in the book can be applied in versions prior to MATLAB 2017b and Python 3.7.4 without the need to modify these programs.This book is aimed at newcomers and middle-level students, as well as members of the scientific community who are interested in solving math problems using MATLAB or Python.
R 5,652.87
Numerical Computations with GPUs
This book brings together research on numerical methods adapted for Graphics Processing Units (GPUs). It explains recent efforts to adapt classic numerical methods, including solution of linear equations and FFT, for massively parallel GPU architectures. This volume consolidates recent research and adaptations, covering widely used methods that are at the core of many scientific and engineering computations. Each chapter is written by authors working on a specific group of methods; these leading experts provide mathematical background, parallel algorithms and implementation details leading to reusable, adaptable and scalable code fragments. This book also serves as a GPU implementation manual for many numerical algorithms, sharing tips on GPUs that can increase application efficiency. The valuable insights into parallelization strategies for GPUs are supplemented by ready-to-use code fragments. Numerical Computations with GPUs targets professionals and researchers working in high performance computing and GPU programming. Advanced-level students focused on computer science and mathematics will also find this book useful as secondary text book or reference.
R 6,581.20
Numerical Methods of Statistics
This book explains how computer software is designed to perform the tasks required for sophisticated statistical analysis. For statisticians, it examines the nitty-gritty computational problems behind statistical methods. For mathematicians and computer scientists, it looks at the application of mathematical tools to statistical problems. The first half of the book offers a basic background in numerical analysis that emphasizes issues important to statisticians. The next several chapters cover a broad array of statistical tools, such as maximum likelihood and nonlinear regression. The author also treats the application of numerical tools; numerical integration and random number generation are explained in a unified manner reflecting complementary views of Monte Carlo methods. Each chapter contains exercises that range from simple questions to research problems. Most of the examples are accompanied by demonstration and source code available from the author's website. New in this second edition are demonstrations coded in R, as well as new sections on linear programming and the Nelder–Mead search algorithm.
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Numerical Relativity: Starting from Scratch
Numerical relativity has emerged as the key tool to model gravitational waves - recently detected for the first time - that are emitted when black holes or neutron stars collide. This book provides a pedagogical, accessible, and concise introduction to the subject. Relying heavily on analogies with Newtonian gravity, scalar fields and electromagnetic fields, it introduces key concepts of numerical relativity in a context familiar to readers without prior expertise in general relativity. Readers can explore these concepts by working through numerous exercises, and can see them 'in action' by experimenting with the accompanying Python sample codes, and so develop familiarity with many techniques commonly employed by publicly available numerical relativity codes. This is an attractive, student-friendly resource for short courses on numerical relativity, as well as providing supplementary reading for courses on general relativity and computational physics.
R 2,145.00
Numerical and Analytical Methods for Scientists and Engineers Using Mathematica
The electronic component of the book is based on the widely used and highly praised Mathematica software package.Each chapter of the bookis a Mathematica notebook with links to web-based material.The methods are applied to a range of problems taken from physics and engineering.The book covers elementary and advaned numerical methods used in modern scientific computing.
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Guaranteed Accuracy in Numerical Linear Algebra
There exists a vast literature on numerical methods of linear algebra. In our bibliography list, which is by far not complete, we included some monographs on the subject [46], [15], [32], [39], [11], [21]. The present book is devoted to the theory of algorithms for a single problem of linear algebra, namely, for the problem of solving systems of linear equations with non-full-rank matrix of coefficients. The solution of this problem splits into many steps, the detailed discussion of which are interest ing problems on their own (bidiagonalization of matrices, computation of singular values and eigenvalues, procedures of deflation of singular values, etc. ). Moreover, the theory of algorithms for solutions of the symmetric eigenvalues problem is closely related to the theory of solv ing linear systems (Householder's algorithms of bidiagonalization and tridiagonalization, eigenvalues and singular values, etc. ). It should be stressed that in this book we discuss algorithms which to computer programs having the virtue that the accuracy of com lead putations is guaranteed. As far as the final program product is con cerned, this means that the user always finds an unambiguous solution of his problem. This solution might be of two kinds: 1. Solution of the problem with an estimate of errors, where abso lutely all errors of input data and machine round-offs are taken into account. 2.
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Numerical Recipes in Pascal (First Edition)
Numerical Recipes: The Art of Scientific Computing was first published in 1986 and became an instant classic among scientists, engineers, and social scientists. In this book the original, time-tested programs have been completely reworked into a clear, consistent Pascal style. This represents a significant improvement to the immensely successful programs contained in the first edition, which were originally written in Fortran. The authors make extensive use of pointers, dynamic memory allocation, and other features utilized by this language. The explanatory text accompanying the programs replicates the lucid, and easy-to-read prose found in the original version, and incorporates corrections, improvements, and explanations of special Pascal features. The product of a unique collaboration among four leading scientists in academic research and industry, Numerical Recipes in Pascal fills a long-recognized need for a practical, comprehensive handbook of scientific computing in the Pascal language. The book is designed both for the Pascal programmer who wants exposure to the techniques of scientific computing, and for the working scientist, social scientist, and engineer. The scope of the book ranges from standard areas of numerical analysis (linear algebra, differential equations, roots) through subjects useful to signal processing (Fourier methods, filtering), data analysis (least squares, robust fitting, statistical functions), simulation (random deviates and Monte Carlo), and more. The lively, informal text combined with an underlying degree of mathematical sophistication makes the book useful to a wide range of readers, beginning at the advanced undergraduate level.
R 4,355.15
Numerical Recipes 3rd Edition
Do you want easy access to the latest methods in scientific computing? This greatly expanded third edition of Numerical Recipes has it, with wider coverage than ever before, many new, expanded and updated sections, and two completely new chapters. The executable C++ code, now printed in colour for easy reading, adopts an object-oriented style particularly suited to scientific applications. Co-authored by four leading scientists from academia and industry, Numerical Recipes starts with basic mathematics and computer science and proceeds to complete, working routines. The whole book is presented in the informal, easy-to-read style that made earlier editions so popular. Highlights of the new material include: a new chapter on classification and inference, Gaussian mixture models, HMMs, hierarchical clustering, and SVMs; a new chapter on computational geometry, covering KD trees, quad- and octrees, Delaunay triangulation, and algorithms for lines, polygons, triangles, and spheres; interior point methods for linear programming; MCMC; an expanded treatment of ODEs with completely new routines; and many new statistical distributions. For support, or to subscribe to an online version, please visit www.nr.com.
R 4,844.00
R 733.00
Numerical Computing with Python
Understand, explore, and effectively present data using the powerful data visualization techniques of PythonKey FeaturesUse the power of Pandas and Matplotlib to easily solve data mining issuesUnderstand the basics of statistics to build powerful predictive data modelsGrasp data mining concepts with helpful use-cases and examplesBook DescriptionData mining, or parsing the data to extract useful insights, is a niche skill that can transform your career as a data scientist Python is a flexible programming language that is equipped with a strong suite of libraries and toolkits, and gives you the perfect platform to sift through your data and mine the insights you seek. This Learning Path is designed to familiarize you with the Python libraries and the underlying statistics that you need to get comfortable with data mining.You will learn how to use Pandas, Python's popular library to analyze different kinds of data, and leverage the power of Matplotlib to generate appealing and impressive visualizations for the insights you have derived. You will also explore different machine learning techniques and statistics that enable you to build powerful predictive models.By the end of this Learning Path, you will have the perfect foundation to take your data mining skills to the next level and set yourself on the path to become a sought-after data science professional.This Learning Path includes content from the following Packt products:Statistics for Machine Learning by Pratap DangetiMatplotlib 2.x By Example by Allen Yu, Claire Chung, Aldrin YimPandas Cookbook by Theodore PetrouWhat you will learnUnderstand the statistical fundamentals to build data modelsSplit data into independent groups Apply aggregations and transformations to each groupCreate impressive data visualizationsPrepare your data and design models Clean up data to ease data analysis and visualizationCreate insightful visualizations with Matplotlib and SeabornCustomize the model to suit your own predictive goalsWho this book is forIf you want to learn how to use the many libraries of Python to extract impactful information from your data and present it as engaging visuals, then this is the ideal Learning Path for you. Some basic knowledge of Python is enough to get started with this Learning Path.
R 1,628.00
Neural Networks and Numerical Analysis
This book uses numerical analysis as the main tool to investigate methods in machine learning and neural networks. The efficiency of neural network representations for general functions and for polynomial functions is studied in detail, together with an original description of the Latin hypercube method and of the ADAM algorithm for training. Furthermore, unique features include the use of Tensorflow for implementation session, and the description of on going research about the construction of new optimized numerical schemes.
R 7,537.38
Numerical Methods with Python
Introduces students to appropriate use of computer programming within the scientific disciplines using Python. Discusses several common applications of programming and implementation using real world examples and hands on programming exercises. Students learn how to model situations such as image recognition, medical diagnosis, spread of disease, and others. The text could be used by students and lecturers for courses in Python, Numerical Methods, or as a first course in Data Science.
R 2,097.00
Numerical Linear Algebra
This book offers an introduction to the algorithmic-numerical thinking using basic problems of linear algebra. By focusing on linear algebra, it ensures a stronger thematic coherence than is otherwise found in introductory lectures on numerics. The book highlights the usefulness of matrix partitioning compared to a component view, leading not only to a clearer notation and shorter algorithms, but also to significant runtime gains in modern computer architectures. The algorithms and accompanying numerical examples are given in the programming environment MATLAB, and additionally – in an appendix – in the future-oriented, freely accessible programming language Julia. This book is suitable for a two-hour lecture on numerical linear algebra from the second semester of a bachelor's degree in mathematics.
R 2,202.80
