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C Programming Absolute Beginner's Guide
Updated for C11 Write powerful C programs...without becoming a technical expert! This book is the fastest way to get comfortable with C, one incredibly clear and easy step at a time. You’ll learn all the basics: how to organize programs, store and display data, work with variables, operators, I/O, pointers, arrays, functions, and much more. C programming has neverbeen this simple! Who knew how simple C programming could be? This is today’s best beginner’s guide to writing C programs–and to learning skills you can use with practically any language. Its simple, practical instructions will help you start creating useful, reliable C code, from games to mobile apps. Plus, it’s fully updated for the new C11 standard and today’s free, open source tools! Here’s a small sample of what you’ll learn: • Discover free C programming tools for Windows, OS X, or Linux • Understand the parts of a C program and how they fit together • Generate output and display it on the screen • Interact with users and respond to their input • Make the most of variables by using assignments and expressions • Control programs by testing data and using logical operators • Save time and effort by using loops and other techniques • Build powerful data-entry routines with simple built-in functions • Manipulate text with strings • Store information, so it’s easy to access and use • Manage your data with arrays, pointers, and data structures • Use functions to make programs easier to write and maintain • Let C handle all your program’s math for you • Handle your computer’s memory as efficiently as possible • Make programs more powerful with preprocessing directives
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Personal Finance with Python
Deal with data, build up financial formulas in code from scratch, and evaluate and think about money in your day-to-day life. This book is about Python and personal finance and how you can effectively mix the two together. In Personal Finance with Python you will learn Python and finance at the same time by creating a profit calculator, a currency converter, an amortization schedule, a budget, a portfolio rebalancer, and a purchase forecaster. Many of the examples use pandas, the main data manipulation tool in Python. Each chapter is hands-on, self-contained, and motivated by fun and interesting examples.Although this book assumes a minimal familiarity with programming and the Python language, if you don't have any, don't worry. Everything is built up piece-by-piece and the first chapters are conducted at a relaxed pace. You'll need Python 3.6 (or above) and all of the setup details are included.What You'll LearnWork with data in pandasCalculate Net Present Value and Internal Rate ReturnQuery a third-party API with RequestsManage secretsBuild efficient loopsParse English sentences with RecurrentWork with the YAML file formatFetch stock quotes and use Prophet to forecast the futureWho This Book Is ForAnyone interested in Python, personal finance, and/or both! This book is geared towards those who want to manage their money more effectively and to those who just want to learn or improve their Python.
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Physics with Excel and Python
This book is intended to serve as a basic introduction to scientific computing by treating problems from various areas of physics - mechanics, optics, acoustics, and statistical reasoning in the context of the evaluation of measurements. After working through these examples, students are able to independently work on physical problems that they encounter during their studies. For every exercise, the author introduces the physical problem together with a data structure that serves as an interface to programming in Excel and Python. When a solution is achieved in one application, it can easily be translated into the other one and presumably any other platform for scientific computing. This is possible because the basic techniques of vector and matrix calculation and array broadcasting are also achieved with spreadsheet techniques, and logical queries and for-loops operate on spreadsheets from simple Visual Basic macros. So, starting to learn scientific calculation with Excel, e.g., at High School, is a targeted road to scientific computing. The primary target groups of this book are students with a major or minor subject in physics, who have interest in computational techniques and at the same time want to deepen their knowledge of physics. Math, physics and computer science teachers and Teacher Education students will also find a companion in this book to help them integrate computer techniques into their lessons. Even professional physicists who want to venture into Scientific Computing may appreciate this book.
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Foundations of Data Science with Python
Foundations of Data Science with Python introduces readers to the fundamentals of data science, including data manipulation and visualization, probability, statistics, and dimensionality reduction. This book is targeted toward engineers and scientists, but it should be readily understandable to anyone who knows basic calculus and the essentials of computer programming. It uses a computational-first approach to data science: the reader will learn how to use Python and the associated data-science libraries to visualize, transform, and model data, as well as how to conduct statistical tests using real data sets. Rather than relying on obscure formulas that only apply to very specific statistical tests, this book teaches readers how to perform statistical tests via resampling; this is a simple and general approach to conducting statistical tests using simulations that draw samples from the data being analyzed. The statistical techniques and tools are explained and demonstrated using a diverse collection of data sets to conduct statistical tests related to contemporary topics, from the effects of socioeconomic factors on the spread of the COVID-19 virus to the impact of state laws on firearms mortality.This book can be used as an undergraduate textbook for an Introduction to Data Science course or to provide a more contemporary approach in courses like Engineering Statistics. However, it is also intended to be accessible to practicing engineers and scientists who need to gain foundational knowledge of data science.Key Features: Applies a modern, computational approach to working with dataUses real data sets to conduct statistical tests that address a diverse set of contemporary issuesTeaches the fundamentals of some of the most important tools in the Python data-science stackProvides a basic, but rigorous, introduction to Probability and its application to StatisticsOffers an accompanying website that provides a unique set of online, interactive tools to help the reader learn the material
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CNC Machining Handbook: Building, Programming, and Implementation
Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product.A Practical Guide to CNC MachiningGet a thorough explanation of the entire CNC process from start to finish, including the various machines and their uses and the necessary software and tools. CNC Machining Handbook describes the steps involved in building a CNC machine to custom specifications and successfully implementing it in a real-world application. Helpful photos and illustrations are featured throughout. Whether you're a student, hobbyist, or business owner looking to move from a manual manufacturing process to the accuracy and repeatability of what CNC has to offer, you'll benefit from the in-depth information in this comprehensive resource.CNC Machining Handbook covers:Common types of home and shop-based CNC-controlled applicationsLinear motion guide systemsTransmission systemsStepper and servo motorsController hardwareCartesian coordinate systemCAD (computer-aided drafting) and CAM (computer-aided manufacturing) softwareOverview of G code languageReady-made CNC systems
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Practical Data Science with Python 3
Gain insight into essential data science skills in a holistic manner using data engineering and associated scalable computational methods. This book covers the most popular Python 3 frameworks for both local and distributed (in premise and cloud based) processing. Along the way, you will be introduced to many popular open-source frameworks, like, SciPy, scikitlearn, Numba, Apache Spark, etc. The book is structured around examples, so you will grasp core concepts via case studies and Python 3 code.As data science projects gets continuously larger and more complex, software engineering knowledge and experience is crucial to produce evolvable solutions. You'll see how to create maintainable software for data science and how to document data engineering practices.This book is a good starting point for people who want to gain practical skills to perform data science. All the code will be available in the form of IPython notebooks and Python 3 programs, which allow you to reproduce all analyses from the book and customize them for your own purpose. You'll also benefit from advanced topics like Machine Learning, Recommender Systems, and Security in Data Science.Practical Data Science with Python will empower you analyze data, formulate proper questions, and produce actionable insights, three core stages in most data science endeavors.What You'll LearnPlay the role of a data scientist when completing increasingly challenging exercises using Python 3Work work with proven data science techniques/technologies Review scalable software engineering practices to ramp up data analysis abilities in the realm of Big DataApply theory of probability, statistical inference, and algebra to understand the data science practicesWho This Book Is ForAnyone who would like to embark into the realm of data science using Python 3.
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R 550.00
Programming in Parallel with CUDA
CUDA is now the dominant language used for programming GPUs, one of the most exciting hardware developments of recent decades. With CUDA, you can use a desktop PC for work that would have previously required a large cluster of PCs or access to a HPC facility. As a result, CUDA is increasingly important in scientific and technical computing across the whole STEM community, from medical physics and financial modelling to big data applications and beyond. This unique book on CUDA draws on the author's passion for and long experience of developing and using computers to acquire and analyse scientific data. The result is an innovative text featuring a much richer set of examples than found in any other comparable book on GPU computing. Much attention has been paid to the C++ coding style, which is compact, elegant and efficient. A code base of examples and supporting material is available online, which readers can build on for their own projects.
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Design Multi-Agent AI Systems Using MCP and A2A
Build a production-ready multi-agent AI framework from scratch using MCP and A2A to orchestrate powerful agent workflowsFree with your book: DRM-free PDF version + access to Packt's next-gen Reader*Key FeaturesBuild Python-based AI agents without relying on third-party orchestration frameworksDesign production-ready multi-agent systems using A2A messagingIntegrate memory and context with MCP to create adaptive and stateful agentic AI frameworksBook DescriptionFrustrated by opaque agent frameworks that hide how things work? This book gives you complete control by guiding you through building a fully functional, extensible agentic AI framework in Python without relying on external orchestration tools.You’ll begin by implementing a simple tool-using agent, and then gradually extend its capabilities with structured tool schemas, user interfaces, and memory via the Model Context Protocol (MCP). From there, you’ll build collaborative multi-agent systems powered by Agent-to-Agent (A2A) messaging and deploy them in realistic environments. Along the way, you’ll explore secure tool invocation, message routing, observability, and human-in-the-loop workflows.With annotated code, deep engineering insights, and practical deployment patterns, this hands-on guide equips you to build AI agents that reason, plan, act, and adapt, whether you’re shipping production systems or experimenting with cutting-edge LLM-based architectures.Written by Gigi Sayfan, who builds AI agent infrastructure at Perplexity and is a bestselling author with decades of experience in AI and distributed systems, this book gives you the tools and knowledge to engineer your own advanced agentic systems.*Email sign-up and proof of purchase requiredWhat you will learnDesign and implement tool-using AI agents from the ground upBuild modular components for extensible agent frameworksCreate secure and observable tools with structured inputsIntegrate agents with chat UIs such as Slack and ChainlitLeverage MCP for context handling and agent memoryOrchestrate collaborative agent workflows using A2ADebug and deploy agents in production-like environmentsExplore future-ready agent capabilities and GenUX designWho this book is forThis book is essential for AI engineers, ML practitioners, and software architects building agentic systems with large language models. It’s also ideal for DevOps engineers and technical leaders seeking deep insights into building and scaling autonomous AI workflows. Python coding skills and basic familiarity with LLMs are recommended.
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C Programming: The Essentials for Engineers and Scientists
This text teaches the essentials of C programming, concentrating on what readers need to know in order to produce stand-alone programs and so solve typical scientific and engineering problems. It is a learning-by-doing book, with many examples and exercises, and lays a foundation of scientific programming concepts and techniques that will prove valuable for those who might eventually move on to another language. Written for undergraduates who are familiar with computers and typical applications but are new to programming.
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Introduction To Time-dependent Quantum Mechanics With Python
Computational spectroscopy and computational quantum chemical dynamics is a vast field in physical chemistry. Significant part of this field is developed based on the concepts of time-dependent quantum mechanics and its numerical implementations.This book gives an introduction to the Time-Dependent Quantum Chemistry for use with any introductory college/university course in optics, spectroscopy, kinetics, dynamics, or experimental physical chemistry or chemical physics of the kind usually taken by undergraduate and graduate students in physical chemistry. In this book, different concepts of time-dependent quantum mechanics are systematically presented by first giving emphasis on the contrasting viewpoint of classical and quantum mechanical motion of a particle, then by demonstrating the ways to find classical flavour in quantum dynamics, thereafter by formally defining the wavepacket which represents a quantum particle and finally by demonstrating numerical methods to explore the wavepacket dynamics in one dimension. Along with the analytical theory, accompanying Python chapters in this book take readers to a hands-on tour with Python programming by first giving them a quick introduction to the Python programming, then by introducing the position-space grid representation of the wavefunction, thereafter, by making them familiarized with the Fourier transform to represent the discretized wavefunction in momentum space, subsequently by showing the Python-based methodologies to express Hamiltonian operator in matrix form and finally by demonstrating the entire Python program which solves the wavepacket dynamics in one dimension under influence of time-independent Hamiltonian following split-operator approach.Rigorous class-testing of the presented lecture notes at the Indian Institute of Science, GITAM University and at NPTEL platform reveals that physical chemistry students, after thoroughly going through all chapters, not only develop an in-depth understanding of the wavepacket dynamics and its numerical implementations, but also start successfully writing their own Python code for solving any one dimensional wavepacket dynamics problem.
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Programming for Computations - MATLAB/Octave
This book presents computer programming as a key method for solving mathematical problems. There are two versions of the book, one for MATLAB and one for Python. The book was inspired by the Springer book TCSE 6: A Primer on Scientific Programming with Python (by Langtangen), but the style is more accessible and concise, in keeping with the needs of engineering students. The book outlines the shortest possible path from no previous experience with programming to a set of skills that allows the students to write simple programs for solving common mathematical problems with numerical methods in engineering and science courses. The emphasis is on generic algorithms, clean design of programs, use of functions, and automatic tests for verification.
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Handbook of Computer Troubleshooting
Intended to be more than just a quick-fix manual for the do-it-yourselfer, this handbook covers all aspects of small business computing. It is a complete guide for solving the most typical problems most users will encounter. Both the neophyte and experienced user should find helpful tips. Topics covered include: hardware; graphics software; the Internet; ergonomics; keyboards; networks; company addresses; utilities software; educational software; printers; monitors; security threats; and Websites.
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Introduction to Programming with Fortran
This fourth Edition presents new examples on submodules, derived type i/o, object oriented programming, abstract interfaces and procedure pointers, C interop, sorting and searching, statistics and converting to more modern versions of Fortran.Key FeaturesHighlights the core language features of modern Fortran including data typing, array processing, control structures, functions, subroutines, modules and submodules, user defined types, pointers, operator overloading, generic programming, parallel programming, abstract interfaces, procedure pointersPinpoints common problems that occur when programmingIllustrates the use of several compilersIntroduction to Programming with Fortran has been written for the complete beginner with little or no programming background as well as existing Fortran programmers and those with programming experience in other languages
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Learn Programming with C
Authored by two standout professors in the field of Computer Science and Technology with extensive experience in instructing, Learn Programming with C is a comprehensive and accessible guide to programming with one of the most popular languages. Meticulously illustrated with figures and examples, this book is a comprehensive guide to writing, editing and executing C programs on different operating systems and platforms, as well as how to embed C programs into other applications and how to create one’s own library. A variety of questions and exercises are included in each chapter to test the readers’ knowledgeWritten for the novice C programmer, especially undergraduate and graduate students, this book’s line-by-line explanation of code and succinct writing style makes it an excellent companion for classroom teaching, learning and programming labs.
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R 519.00
Foundations of Data Science with Python
Foundations of Data Science with Python introduces readers to the fundamentals of data science, including data manipulation and visualization, probability, statistics, and dimensionality reduction. This book is targeted toward engineers and scientists, but it should be readily understandable to anyone who knows basic calculus and the essentials of computer programming. It uses a computational-first approach to data science: the reader will learn how to use Python and the associated data-science libraries to visualize, transform, and model data, as well as how to conduct statistical tests using real data sets. Rather than relying on obscure formulas that only apply to very specific statistical tests, this book teaches readers how to perform statistical tests via resampling; this is a simple and general approach to conducting statistical tests using simulations that draw samples from the data being analyzed. The statistical techniques and tools are explained and demonstrated using a diverse collection of data sets to conduct statistical tests related to contemporary topics, from the effects of socioeconomic factors on the spread of the COVID-19 virus to the impact of state laws on firearms mortality.This book can be used as an undergraduate textbook for an Introduction to Data Science course or to provide a more contemporary approach in courses like Engineering Statistics. However, it is also intended to be accessible to practicing engineers and scientists who need to gain foundational knowledge of data science.Key Features: Applies a modern, computational approach to working with dataUses real data sets to conduct statistical tests that address a diverse set of contemporary issuesTeaches the fundamentals of some of the most important tools in the Python data-science stackProvides a basic, but rigorous, introduction to Probability and its application to StatisticsOffers an accompanying website that provides a unique set of online, interactive tools to help the reader learn the material
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R 531.00
Introduction to Modern Scientific Programming and Numerical Methods
The ability to use computers to solve mathematical relationships is a fundamental skill for anyone planning a career in science or engineering. For this reason, numerical analysis is part of the core curriculum for just about every undergraduate physics and engineering department. But for most physics and engineering students, practical programming is a self-taught process.This book introduces the reader not only to the mathematical foundation but also to the programming paradigms encountered in modern hybrid software-hardware scientific computing. After completing the text, the reader will be well-versed in the use of different numerical techniques, programming languages, and hardware architectures, and will be able to select the appropriate software and hardware tool for their analysis.It can serve as a textbook for undergraduate courses on numerical analysis and scientific computing courses within engineering and physical sciences departments. It will also be a valuable guidebook for researchers with experimental backgrounds interested in working with numerical simulations, or for any new personnel working in scientific computing or data analysis.Key Features:• Includes examples of solving numerical problems in multiple programming languages, including MATLAB, Python, Fortran, C++, Arduino, Javascript, and Verilog.• Provides an introduction to modern high-performance computing technologies including multithreading, distributed computing, GPUs, microcontrollers, FPGAs, and web "cloud computing".• Contains an overview of numerical techniques not found in other introductory texts including particle methods, finite volume and finite element methods, Vlasov solvers, and molecular dynamics.Companions websites for this book can be accessed at https://plasmasimulationsbyexample.com/ and https://www.scientificprogrammingbook.com/.Lubos Brieda holds a Ph.D. in Aerospace and Mechanical Aerospace Engineering from the George Washington University in Washington, D.C., USA and an M.Sc. in Aerospace and Ocean Engineering from Virginia Tech in Blacksburg, VA, USA. He is the president of Particle in Cell Consulting, LLC, Westlake Village, CA, USA, while also serving as a part-time lecturer in the Department of Astronautical Engineering at University of Southern California, Los Angeles, CA. Dr. Brieda is the author of numerous simulation codes utilized by the aerospace and plasma modeling communities. Additionally, he maintains an online blog found at particleincell.com/blog focusing on scientific computing and since 2014 he has been teaching online courses on plasma simulations through his website.Joseph Wang is a Professor of Astronautics and Aerospace and Mechanical Engineering at the University of Southern California. Prof. Wang received his Ph.D in Aeronautics and Astronautics from Massachusetts Institute of Technology in 1991. Prof. Wang conducts research in computational physics, space technology, and space and planetary science. He and his students have
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Financial Theory with Python
Nowadays, finance, mathematics, and programming are intrinsically linked. This book provides the relevant foundations of each discipline to give you the major tools you need to get started in the world of computational finance.Using an approach where mathematical concepts provide the common background against which financial ideas and programming techniques are learned, this practical guide teaches you the basics of financial economics. Written by the best-selling author of Python for Finance, Yves Hilpisch, Financial Theory with Python explains financial, mathematical, and Python programming concepts in an integrative manner so that the interdisciplinary concepts reinforce each other.Draw upon mathematics to learn the foundations of financial theory and Python programmingLearn about financial theory, financial data modeling, and the use of Python for computational financeLeverage simple economic models to better understand basic notions of finance and Python programming conceptsUse both static and dynamic financial modeling to address fundamental problems in finance, such as pricing, decision-making, equilibrium, and asset allocationLearn the basics of Python packages useful for financial modeling, such as NumPy, pandas, Matplotlib, and SymPy
R 1,277.00
Hands-On Image Processing with Python
Explore the mathematical computations and algorithms for image processing using popular Python tools and frameworks.Key FeaturesPractical coverage of every image processing task with popular Python librariesIncludes topics such as pseudo-coloring, noise smoothing, computing image descriptorsCovers popular machine learning and deep learning techniques for complex image processing tasksBook DescriptionImage processing plays an important role in our daily lives with various applications such as in social media (face detection), medical imaging (X-ray, CT-scan), security (fingerprint recognition) to robotics & space. This book will touch the core of image processing, from concepts to code using Python.The book will start from the classical image processing techniques and explore the evolution of image processing algorithms up to the recent advances in image processing or computer vision with deep learning. We will learn how to use image processing libraries such as PIL, scikit-mage, and scipy ndimage in Python. This book will enable us to write code snippets in Python 3 and quickly implement complex image processing algorithms such as image enhancement, filtering, segmentation, object detection, and classification. We will be able to use machine learning models using the scikit-learn library and later explore deep CNN, such as VGG-19 with Keras, and we will also use an end-to-end deep learning model called YOLO for object detection. We will also cover a few advanced problems, such as image inpainting, gradient blending, variational denoising, seam carving, quilting, and morphing.By the end of this book, we will have learned to implement various algorithms for efficient image processing.What you will learnPerform basic data pre-processing tasks such as image denoising and spatial filtering in PythonImplement Fast Fourier Transform (FFT) and Frequency domain filters (e.g., Weiner) in PythonDo morphological image processing and segment images with different algorithmsLearn techniques to extract features from images and match imagesWrite Python code to implement supervised / unsupervised machine learning algorithms for image processingUse deep learning models for image classification, segmentation, object detection and style transferWho this book is forThis book is for Computer Vision Engineers, and machine learning developers who are good with Python programming and want to explore details and complexities of image processing. No prior knowledge of the image processing techniques is expected.
R 1,661.00
