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R 641.00
R 762.00
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
R 611.00
R 794.00
Python GUI Programming - A Complete Reference Guide
Explore Python’s GUI frameworks and create visually stunning and feature-rich applicationsKey FeaturesIntegrate stunning data visualizations using Tkinter Canvas and MatplotlibUnderstand the basics of 2D and 3D animation in GUI applicationsExplore PyQt’s powerful features to easily design and customize your GUI applicationsBook DescriptionA responsive graphical user interface (GUI) helps you interact with your application, improves user experience, and enhances the efficiency of your applications. With Python, you’ll have access to elaborate GUI frameworks that you can use to build interactive GUIs that stand apart from the rest.This Learning Path begins by introducing you to Tkinter and PyQt, before guiding you through the application development process. As you expand your GUI by adding more widgets, you'll work with networks, databases, and graphical libraries that enhance its functionality. You'll also learn how to connect to external databases and network resources, test your code, and maximize performance using asynchronous programming. In later chapters, you'll understand how to use the cross-platform features of Tkinter and Qt5 to maintain compatibility across platforms. You’ll be able to mimic the platform-native look and feel, and build executables for deployment across popular computing platforms.By the end of this Learning Path, you'll have the skills and confidence to design and build high-end GUI applications that can solve real-world problems.This Learning Path includes content from the following Packt products:Python GUI Programming with Tkinter by Alan D. MooreQt5 Python GUI Programming Cookbook by B. M. HarwaniWhat you will learnVisualize graphs in real time with Tkinter’s animation capabilitiesUse PostgreSQL authentication to ensure data security for your applicationWrite unit tests to avoid regression when updating codeHandle different signals generated on mouse clicks using QSpinBox and slidersEmploy network concepts, internet browsing, and Google Maps in UIUse graphics rendering to implement animations in your GUIWho this book is forIf you’re an intermediate Python programmer looking to enhance your coding skills by writing powerful GUIs in Python using PyQT and Tkinter, this is an ideal Learning Path for you. A strong understanding of the Python language is a must to grasp the concepts explained in this book.
R 2,309.58
50 Algorithms Every Programmer Should Know - Second Edition
Delve into the realm of generative AI and large language models (LLMs) while exploring modern deep learning techniques, including LSTMs, GRUs, RNNs with new chapters included in this 50% new edition overhaulPurchase of the print or Kindle book includes a free eBook in PDF format.Key FeaturesFamiliarize yourself with advanced deep learning architecturesExplore newer topics, such as handling hidden bias in data and algorithm explainabilityGet to grips with different programming algorithms and choose the right data structures for their optimal implementationBook DescriptionThe ability to use algorithms to solve real-world problems is a must-have skill for any developer or programmer. This book will help you not only to develop the skills to select and use an algorithm to tackle problems in the real world but also to understand how it works.You'll start with an introduction to algorithms and discover various algorithm design techniques, before exploring how to implement different types of algorithms, with the help of practical examples. As you advance, you'll learn about linear programming, page ranking, and graphs, and will then work with machine learning algorithms to understand the math and logic behind them.Case studies will show you how to apply these algorithms optimally before you focus on deep learning algorithms and learn about different types of deep learning models along with their practical use.You will also learn about modern sequential models and their variants, algorithms, methodologies, and architectures that are used to implement Large Language Models (LLMs) such as ChatGPT.Finally, you'll become well versed in techniques that enable parallel processing, giving you the ability to use these algorithms for compute-intensive tasks.By the end of this programming book, you'll have become adept at solving real-world computational problems by using a wide range of algorithms.What you will learnDesign algorithms for solving complex problemsBecome familiar with neural networks and deep learning techniquesExplore existing data structures and algorithms found in Python librariesImplement graph algorithms for fraud detection using network analysisDelve into state-of-the-art algorithms for proficient Natural Language Processing illustrated with real-world examplesCreate a recommendation engine that suggests relevant movies to subscribersGrasp the concepts of sequential machine learning models and their foundational role in the development of cutting-edge LLMsWho this book is forThis computer science book is for programmers or developers who want to understand the use of algorithms for problem-solving and writing efficient code.Whether you are a beginner looking to learn the most used algorithms concisely or an experienced programmer looking to explore cutting-edge algorithms in data science, machine learning, and cryptography, you'll find this book useful.Python programming experience is a must, knowledge of data science will be helpful but not n
R 2,442.00
R 886.00
Numerical Methods of Mathematics Implemented in Fortran
This book systematically classifies the mathematical formalisms of computational models that are required for solving problems in mathematics, engineering and various other disciplines. It also provides numerical methods for solving these problems using suitable algorithms and for writing computer codes to find solutions. For discrete models, matrix algebra comes into play, while for continuum framework models, real and complex analysis is more suitable. The book clearly describes the method–algorithm–code approach for learning the techniques of scientific computation and how to arrive at accurate solutions by applying the procedures presented. It not only provides instructors with course material but also serves as a useful reference resource. Providing the detailed mathematical proofs behind the computational methods, this book appeals to undergraduate and graduate mathematics and engineering students.The computer codes have been written in the Fortran programming language, which is the traditional language for scientific computation. Fortran has a vast repository of source codes used in real-world applications and has continuously been upgraded in line with the computing capacity of the hardware. The language is fully backwards compatible with its earlier versions, facilitating integration with older source codes.
R 2,585.82
R 550.00
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 244.00
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.
R 4,030.00
R 550.00
Mathematical Programming Methods for Geographers and Planners
Originally published in 1983, this was the first text to offer an in-depth treatment of mathematical programming methods explained from first principles. It considers all the major programming techniques and fully explains key terms, illustrates theories with detailed examples and shows how the various skills are applied in practice. It will be invaluable in both the academic world and to policy formulators and planners, who make extensive use of the methods described.
R 6,960.00
Python Network Programming
Power up your network applications with Python programmingKey FeaturesMaster Python skills to develop powerful network applicationsGrasp the fundamentals and functionalities of SDNDesign multi-threaded, event-driven architectures for echo and chat serversBook DescriptionThis Learning Path highlights major aspects of Python network programming such as writing simple networking clients, creating and deploying SDN and NFV systems, and extending your network with Mininet. You’ll also learn how to automate legacy and the latest network devices. As you progress through the chapters, you’ll use Python for DevOps and open source tools to test, secure, and analyze your network. Toward the end, you'll develop client-side applications, such as web API clients, email clients, SSH, and FTP, using socket programming.By the end of this Learning Path, you will have learned how to analyze a network's security vulnerabilities using advanced network packet capture and analysis techniques. This Learning Path includes content from the following Packt products:Practical Network Automation by Abhishek RatanMastering Python Networking by Eric ChouPython Network Programming Cookbook, Second Edition by Pradeeban Kathiravelu, Dr. M. O. Faruque SarkerWhat you will learnCreate socket-based networks with asynchronous modelsDevelop client apps for web APIs, including S3 Amazon and TwitterTalk to email and remote network servers with different protocolsIntegrate Python with Cisco, Juniper, and Arista eAPI for automationUse Telnet and SSH connections for remote system monitoringInteract with websites via XML-RPC, SOAP, and REST APIsBuild networks with Ryu, OpenDaylight, Floodlight, ONOS, and POXConfigure virtual networks in different deployment environmentsWho this book is forIf you are a Python developer or a system administrator who wants to start network programming, this Learning Path gets you a step closer to your goal. IT professionals and DevOps engineers who are new to managing network devices or those with minimal experience looking to expand their knowledge and skills in Python will also find this Learning Path useful. Although prior knowledge of networking is not required, some experience in Python programming will be helpful for a better understanding of the concepts in the Learning Path.
R 1,628.00
Discovering Mathematics with Magma
Based on the ontology and semantics of algebra, the computer algebra system Magma enables users to rapidly formulate and perform calculations in abstract parts of mathematics. Edited by the principal designers of the program, this book explores Magma. Coverage ranges from number theory and algebraic geometry, through representation theory and group theory to discrete mathematics and graph theory. Includes case studies describing computations underpinning new theoretical results.
R 4,131.67
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
Computer-Assisted Analysis System for Mathematical Programming Models and Solutions
Welcome to ANALYZE, designed to provide computer assistance for analyzing linear programs and their solutions. Chapter 1 gives an overview of ANALYZE and how to install it. It also describes how to get started and how to obtain further documentation and help on-line. Chapter 2 reviews the forms of linear programming models and describes the syntax of a model. One of the routine, but important, functions of ANALYZE is to enable convenient access to rows and columns in the matrix by conditional delineation. Chapter 3 illustrates simple queries, like DISPLAY, LIST, and PICTURE. This chapter also introduces the SUBMAT command level to define any submatrix by an arbitrary sequence of additions, deletions and reversals. Syntactic explanations and a schema view are also illustrated. Chapter 4 goes through some elementary exercises to demonstrate computer assisted analysis and introduce additional conventions of the ANALYZE language. Besides simple queries, it demonstrates the INTERPRT command, which automates the analysis process and gives English explanations of results. The last 2 exercises are diagnoses of elementary infeasible instances of a particular model. Chapter 5 progresses to some advanced uses of ANALYZE. The first is blocking to obtain macro views of the model and for finding embedded substructures, like a netform. The second is showing rates of substitution described by the basic equations. Then, the use of the REDUCE and BASIS commands are illustrated for a variety of applications, including solution analysis, infeasibility diagnosis, and redundancy detection.
R 4,651.64
Foundations for Analytics with Python
If you're like many of Excel's 750 million users, you want to do more with your data-like repeating similar analyses over hundreds of files, or combining data in many files for analysis at one time. This practical guide shows ambitious non-programmers how to automate and scale the processing and analysis of data in different formats-by using Python. After author Clinton Brownley takes you through Python basics, you'll be able to write simple scripts for processing data in spreadsheets as well as databases. You'll also learn how to use several Python modules for parsing files, grouping data, and producing statistics. No programming experience is necessary.Create and run your own Python scripts by learning basic syntax Use Python's csv module to read and parse CSV files Read multiple Excel worksheets and workbooks with the xlrd module Perform database operations in MySQL or with the mysqlclient module Create Python applications to find specific records, group data, and parse text files Build statistical graphs and plots with matplotlib, pandas, ggplot, and seaborn Produce summary statistics, and estimate regression and classification models Schedule your scripts to run automatically in both Windows and Mac environments
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R 397.00
Python Polars: The Definitive Guide
Want to speed up your data analysis and work with larger-than-memory datasets? Python Polars offers a blazingly fast, multithreaded, and elegant API for data loading, manipulation, and processing. With this hands-on guide, you'll walk through every aspect of Polars and learn how to tackle practical use cases using real-world datasets. Jeroen Janssens and Thijs Nieuwdorp from Xomnia in Amsterdam show you how this superfast DataFrame library is perfect for efficient data wrangling, ETL pipelines, and so much more. This book helps you quickly learn the syntax and understand Polars' underlying concepts. You don't need to have experience with pandas or Spark, but if you do, this book will help you make a smooth transition. With this definitive guide at your side, you'll be able to: Process larger-than-memory datasets at record speed Apply the eager, lazy, and streaming APIs of Polars and decide when to use them Transition smoothly from pandas or Spark to Polars Integrate Polars into your existing code base Work with Arrow and Parquet to efficiently read and write data Translate complex ETL tasks into efficient and elegant queries
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R 1,854.50
R 912.00
Mathematical Methods in Engineering
This text focuses on a variety of topics in mathematics in common usage in graduate engineering programs including vector calculus, linear and nonlinear ordinary differential equations, approximation methods, vector spaces, linear algebra, integral equations and dynamical systems. The book is designed for engineering graduate students who wonder how much of their basic mathematics will be of use in practice. Following development of the underlying analysis, the book takes students through a large number of examples that have been worked in detail. Students can choose to go through each step or to skip ahead if they so desire. After seeing all the intermediate steps, they will be in a better position to know what is expected of them when solving assignments, examination problems, and when on the job. Chapters conclude with exercises for the student that reinforce the chapter content and help connect the subject matter to a variety of engineering problems. Students have grown up with computer-based tools including numerical calculations and computer graphics; the worked-out examples as well as the end-of-chapter exercises often use computers for numerical and symbolic computations and for graphical display of the results.
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Mathematical Programming and Financial Objectives for Scheduling Projects
Mathematical Programming and Financial Objectives for Scheduling Projects focuses on decision problems where the performance is measured in terms of money. As the title suggests, special attention is paid to financial objectives and the relationship of financial objectives to project schedules and scheduling. In addition, how schedules relate to other decisions is treated in detail. The book demonstrates that scheduling must be combined with project selection and financing, and that scheduling helps to give an answer to the planning issue of the amount of resources required for a project. The author makes clear the relevance of scheduling to cutting budget costs. The book is divided into six parts. The first part gives a brief introduction to project management. Part two examines scheduling projects in order to maximize their net present value. Part three considers capital rationing. Many decisions on selecting or rejecting a project cannot be made in isolation and multiple projects must be taken fully into account. Since the requests for capital resources depend on the schedules of the projects, scheduling taken on more complexity. Part four studies the resource usage of a project in greater detail. Part five discusses cases where the processing time of an activity is a decision to be made. Part six summarizes the main results that have been accomplished.
R 1,540.07
Using Mathematica for Quantum Mechanics
This book revisits many of the problems encountered in introductory quantum mechanics, focusing on computer implementations for finding and visualizing analytical and numerical solutions. It subsequently uses these implementations as building blocks to solve more complex problems, such as coherent laser-driven dynamics in the Rubidium hyperfine structure or the Rashba interaction of an electron moving in 2D. The simulations are highlighted using the programming language Mathematica. No prior knowledge of Mathematica is needed; alternatives, such as Matlab, Python, or Maple, can also be used.
R 3,193.55
R 1,618.00
Python For Kids For Dummies
The kid-friendly way to learning coding with Python Calling all wanna-be coders! Experts point to Python as one of the best languages to start with when you're learning coding, and Python For Kids For Dummies makes it easier than ever. Packed with approachable, bite-sized projects that won't make you lose your cool, this fun and friendly guide teaches the basics of coding with Python in a language you can understand. In no time, you'll be installing Python tools, creating guessing games, building a geek speak translator, making a trivia game, constructing a Minecraft chat client, and so much more. Whether you don't have the opportunity to take coding classes at school or in camp—or just simply prefer to learn on your own—Python For Kids For Dummies makes getting acquainted with this popular coding language fast and easy. It walks you step-by-step through basic coding projects and provides lots of hands-on tasks that give you a sweet sense of accomplishment when you complete them. What's not to love about that? Navigate the basics of coding with the Python languageCreate your own applications and gamesFind help from other Python usersExpand your technology skills with Python If you're a pre-to-early-teen looking to add coding skills to your creativity toolbox, Python For Kids For Dummies is your sure-fire weapon for getting up and running with one of the hottest programming languages around.
R 804.00
Mathematics Education Through Technology: Precalculus, Calculus, And More
This book caters to both prospective and current mathematics educators at the school and university levels, along with their students and anyone intrigued by the possibilities of integrating software into mathematics education.Mathematics, being a unified science, is best comprehended when its cohesive nature is emphasized and demonstrated to students. Educational software assumes a pivotal role in achieving this pedagogical goal.The book outlines the author's methodology in utilizing educational software for developing and applying computer models, grounded in a thorough analysis of the subject matter. This approach is vividly illustrated through examples employing a non-profit authoring program (VisuMatica) provided with the book, enabling users to:Through the guidance of this book, students will discover mathematics as clear, visible, friendly, and enjoyable.The author exemplifies this approach by focusing on the functional line of mathematics, spanning from school basics to advanced university courses in higher mathematics. Importantly, the assimilated ideas and techniques are readily applicable to teaching and learning other areas of mathematics.
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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
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R 580.00
Mathematics for Multimedia
This textbook presents the mathematics that is foundational to multimedia applications. Featuring a rigorous survey of selected results from algebra and analysis, the work examines tools used to create application software for multimedia signal processing and communication. Replete with exercises, sample programs in Standard C, and numerous illustrations, Mathematics for Multimedia is an ideal textbook for upper undergraduate and beginning graduate students in computer science and mathematics who seek an innovative approach to contemporary mathematics with practical applications. The work may also serve as an invaluable reference for multimedia applications developers and all those interested in the mathematics underlying multimedia design and implementation.
R 1,670.20
Introduction to Scientific Programming and Simulation Using R
Learn How to Program Stochastic ModelsHighly recommended, the best-selling first edition of Introduction to Scientific Programming and Simulation Using R was lauded as an excellent, easy-to-read introduction with extensive examples and exercises. This second edition continues to introduce scientific programming and stochastic modelling in a clear, practical, and thorough way. Readers learn programming by experimenting with the provided R code and data.The book’s four parts teach: Core knowledge of R and programming conceptsHow to think about mathematics from a numerical point of view, including the application of these concepts to root finding, numerical integration, and optimisationEssentials of probability, random variables, and expectation required to understand simulationStochastic modelling and simulation, including random number generation and Monte Carlo integration In a new chapter on systems of ordinary differential equations (ODEs), the authors cover the Euler, midpoint, and fourth-order Runge-Kutta (RK4) schemes for solving systems of first-order ODEs. They compare the numerical efficiency of the different schemes experimentally and show how to improve the RK4 scheme by using an adaptive step size. Another new chapter focuses on both discrete- and continuous-time Markov chains. It describes transition and rate matrices, classification of states, limiting behaviour, Kolmogorov forward and backward equations, finite absorbing chains, and expected hitting times. It also presents methods for simulating discrete- and continuous-time chains as well as techniques for defining the state space, including lumping states and supplementary variables.Building readers’ statistical intuition, Introduction to Scientific Programming and Simulation Using R, Second Edition shows how to turn algorithms into code. It is designed for those who want to make tools, not just use them. The code and data are available for download from CRAN.
R 5,694.76
R 684.00
R 1,130.00
Numerical Methods of Mathematics Implemented in Fortran
This book systematically classifies the mathematical formalisms of computational models that are required for solving problems in mathematics, engineering and various other disciplines. It also provides numerical methods for solving these problems using suitable algorithms and for writing computer codes to find solutions. For discrete models, matrix algebra comes into play, while for continuum framework models, real and complex analysis is more suitable. The book clearly describes the method–algorithm–code approach for learning the techniques of scientific computation and how to arrive at accurate solutions by applying the procedures presented. It not only provides instructors with course material but also serves as a useful reference resource. Providing the detailed mathematical proofs behind the computational methods, this book appeals to undergraduate and graduate mathematics and engineering students.The computer codes have been written in the Fortran programming language, which is the traditional language for scientific computation. Fortran has a vast repository of source codes used in real-world applications and has continuously been upgraded in line with the computing capacity of the hardware. The language is fully backwards compatible with its earlier versions, facilitating integration with older source codes.
R 2,584.92
