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Advanced R Statistical Programming and Data Models - Joshua F. Wiley - Bog - APress - Plusbog.dk

Advanced R Statistical Programming and Data Models - Joshua F. Wiley - Bog - APress - Plusbog.dk

Carry out a variety of advanced statistical analyses including generalized additive models, mixed effects models, multiple imputation, machine learning, and missing data techniques using R. Each chapter starts with conceptual background information about the techniques, includes multiple examples using R to achieve results, and concludes with a case study. Written by Matt and Joshua F. Wiley, Advanced R Statistical Programming and Data Models shows you how to conduct data analysis using the popular R language. You''ll delve into the preconditions or hypothesis for various statistical tests and techniques and work through concrete examples using R for a variety of these next-level analytics. This is a must-have guide and reference on using and programming with the R language. What You''ll Learn - Conduct advanced analyses in R including: generalized linear models, generalized additive models, mixed effects models, machine learning, and parallel processing - Carry out regression modeling using R data visualization, linear and advanced regression, additive models, survival / time to event analysis - Handle machine learning using R including parallel processing, dimension reduction, and feature selection and classification - Address missing data using multiple imputation in R - Work on factor analysis, generalized linear mixed models, and modeling intraindividual variability Who This Book Is For Working professionals, researchers, or students who are familiar with R and basic statistical techniques such as linear regression and who want to learn how to use R to perform more advanced analytics. Particularly, researchers and data analysts in the social sciences may benefit from these techniques. Additionally, analysts who need parallel processing to speed up analytics are given proven code to reduce time to result(s).

DKK 604.00
1

Learn R for Applied Statistics - Eric Goh Ming Hui - Bog - APress - Plusbog.dk

Learn R for Applied Statistics - Eric Goh Ming Hui - Bog - APress - Plusbog.dk

Gain the R programming language fundamentals for doing the applied statistics useful for data exploration and analysis in data science and data mining. This book covers topics ranging from R syntax basics, descriptive statistics, and data visualizations to inferential statistics and regressions. After learning R''s syntax, you will work through data visualizations such as histograms and boxplot charting, descriptive statistics, and inferential statistics such as t-test, chi-square test, ANOVA, non-parametric test, and linear regressions. Learn R for Applied Statistics is a timely skills-migration book that equips you with the R programming fundamentals and introduces you to applied statistics for data explorations. What You Will Learn - Discover R, statistics, data science, data mining, and big data - Master the fundamentals of R programming, including variables and arithmetic, vectors, lists, data frames, conditional statements, loops, and functions - Work with descriptive statistics - Create data visualizations, including bar charts, line charts, scatter plots, boxplots, histograms, and scatterplots - Use inferential statistics including t-tests, chi-square tests, ANOVA, non-parametric tests, linear regressions, and multiple linear regressions Who This Book Is For Those who are interested in data science, in particular data exploration using applied statistics, and the use of R programming for data visualizations.

DKK 519.00
1

Quantum Computing by Practice - Vladimir Silva - Bog - APress - Plusbog.dk

Quantum Computing by Practice - Vladimir Silva - Bog - APress - Plusbog.dk

Learn to write algorithms and program in the new field of quantum computing. This second edition is updated to equip you with the latest knowledge and tools needed to be a complex problem-solver in this ever-evolving landscape. The book has expanded its coverage of current and future advancements and investments by IT companies in this emerging technology. Most chapters are thoroughly revised to incorporate the latest updates to IBM Quantum's systems and offerings, such as improved algorithms, integrating hardware advancements, software enhancements, bug fixes, and more. You’ll examine quantum computing in the cloud and run experiments there on a real quantum device. Along the way you’ll cover game theory with the Magic Square, an example of quantum pseudo-telepathy. You’ll also learn to write code using QISKit, Python SDK, and other APIs such as QASM and execute it against simulators (local or remote) or a real quantum computer. Then peek inside the inner workings of the Bell states for entanglement, Grover’s algorithm for linear search, Shor’s algorithm for integer factorization, and other algorithms in the fields of optimization, and more. Finally, you’ll learn the current quantum algorithms for entanglement, random number generation, linear search, integer factorization, and others. By the end of this book, you’ll understand how quantum computing provides massive parallelism and significant computational speedups over classical computersWhat You'll LearnWrite algorithms that provide superior performance over their classical counterpartsCreate a quantum number generator: the quintessential coin flip with a quantum twistExamine the quantum algorithms in use today for random number generation, linear search, and moreDiscover quantum teleportationHandle the counterfeit coin problem, a classic puzzle Put your knowledge to the testwith more than 150 practice exercises Who This Book Is ForDevelopers, programmers, computer science researchers, teachers, and students.

DKK 391.00
1

Distributed Machine Learning with PySpark - Abdelaziz Testas - Bog - APress - Plusbog.dk

Distributed Machine Learning with PySpark - Abdelaziz Testas - Bog - APress - Plusbog.dk

Migrate from pandas and scikit-learn to PySpark to handle vast amounts of data and achieve faster data processing time. This book will show you how to make this transition by adapting your skills and leveraging the similarities in syntax, functionality, and interoperability between these tools. Distributed Machine Learning with PySpark offers a roadmap to data scientists considering transitioning from small data libraries (pandas/scikit-learn) to big data processing and machine learning with PySpark. You will learn to translate Python code from pandas/scikit-learn to PySpark to preprocess large volumes of data and build, train, test, and evaluate popular machine learning algorithms such as linear and logistic regression, decision trees, random forests, support vector machines, Naïve Bayes, and neural networks. After completing this book, you will understand the foundational concepts of data preparation and machine learning and will have the skills necessary toapply these methods using PySpark, the industry standard for building scalable ML data pipelines. What You Will LearnMaster the fundamentals of supervised learning, unsupervised learning, NLP, and recommender systemsUnderstand the differences between PySpark, scikit-learn, and pandasPerform linear regression, logistic regression, and decision tree regression with pandas, scikit-learn, and PySparkDistinguish between the pipelines of PySpark and scikit-learn Who This Book Is ForData scientists, data engineers, and machine learning practitioners who have some familiarity with Python, but who are new to distributed machine learning and the PySpark framework.

DKK 434.00
1

Practical Explainable AI Using Python - Pradeepta Mishra - Bog - APress - Plusbog.dk

Practical Explainable AI Using Python - Pradeepta Mishra - Bog - APress - Plusbog.dk

Learn the ins and outs of decisions, biases, and reliability of AI algorithms and how to make sense of these predictions. This book explores the so-called black-box models to boost the adaptability, interpretability, and explainability of the decisions made by AI algorithms using frameworks such as Python XAI libraries, TensorFlow 2.0+, Keras, and custom frameworks using Python wrappers. You'll begin with an introduction to model explainability and interpretability basics, ethical consideration, and biases in predictions generated by AI models. Next, you'll look at methods and systems to interpret linear, non-linear, and time-series models used in AI. The book will also cover topics ranging from interpreting to understanding how an AI algorithm makes a decisionFurther, you will learn the most complex ensemble models, explainability, and interpretability using frameworks such as Lime, SHAP, Skater, ELI5, etc. Moving forward, youwill be introduced to model explainability for unstructured data, classification problems, and natural language processing–related tasks. Additionally, the book looks at counterfactual explanations for AI models. Practical Explainable AI Using Python shines the light on deep learning models, rule-based expert systems, and computer vision tasks using various XAI frameworks. What You'll LearnReview the different ways of making an AI model interpretable and explainableExamine the biasness and good ethical practices of AI modelsQuantify, visualize, and estimate reliability of AI modelsDesign frameworks to unbox the black-box modelsAssess the fairness of AI modelsUnderstand the building blocks of trust in AI modelsIncrease the level of AI adoptionWho This Book Is ForAI engineers, data scientists, and software developers involved in driving AI projects/ AI products.

DKK 519.00
1

Deep Learning with Python - Nikhil Ketkar - Bog - APress - Plusbog.dk

Deep Learning with Python - Nikhil Ketkar - Bog - APress - Plusbog.dk

Master the practical aspects of implementing deep learning solutions with PyTorch, using a hands-on approach to understanding both theory and practice. This updated edition will prepare you for applying deep learning to real world problems with a sound theoretical foundation and practical know-how with PyTorch, a platform developed by Facebook''s Artificial Intelligence Research Group. You''ll start with a perspective on how and why deep learning with PyTorch has emerged as an path-breaking framework with a set of tools and techniques to solve real-world problems. Next, the book will ground you with the mathematical fundamentals of linear algebra, vector calculus, probability and optimization. Having established this foundation, you''ll move on to key components and functionality of PyTorch including layers, loss functions and optimization algorithms. You''ll also gain an understanding of Graphical Processing Unit (GPU) based computation, which is essential for training deep learning models. All the key architectures in deep learning are covered, including feedforward networks, convolution neural networks, recurrent neural networks, long short-term memory networks, autoencoders and generative adversarial networks. Backed by a number of tricks of the trade for training and optimizing deep learning models, this edition of Deep Learning with Python explains the best practices in taking these models to production with PyTorch. What You''ll Learn - Review machine learning fundamentals such as overfitting, underfitting, and regularization. - Understand deep learning fundamentals such as feed-forward networks, convolution neural networks, recurrent neural networks, automatic differentiation, and stochastic gradient descent. - Apply in-depth linear algebra with PyTorch - Explore PyTorch fundamentals and its building blocks - Work with tuning and optimizing models Who This Book Is For Beginners with a working knowledge of Python who want to understand Deep Learning in a practical, hands-on manner.

DKK 294.00
1

Data Science Solutions with Python - Tshepo Chris Nokeri - Bog - APress - Plusbog.dk

Data Science Solutions with Python - Tshepo Chris Nokeri - Bog - APress - Plusbog.dk

Apply supervised and unsupervised learning to solve practical and real-world big data problems. This book teaches you how to engineer features, optimize hyperparameters, train and test models, develop pipelines, and automate the machine learning (ML) process. The book covers an in-memory, distributed cluster computing framework known as PySpark, machine learning framework platforms known as scikit-learn, PySpark MLlib, H2O, and XGBoost, and a deep learning (DL) framework known as Keras. The book starts off presenting supervised and unsupervised ML and DL models, and then it examines big data frameworks along with ML and DL frameworks. Author Tshepo Chris Nokeri considers a parametric model known as the Generalized Linear Model and a survival regression model known as the Cox Proportional Hazards model along with Accelerated Failure Time (AFT). Also presented is a binary classification model (logistic regression) and an ensemble model (Gradient Boosted Trees). The book introduces DL and an artificial neural network known as the Multilayer Perceptron (MLP) classifier. A way of performing cluster analysis using the K-Means model is covered. Dimension reduction techniques such as Principal Components Analysis and Linear Discriminant Analysis are explored. And automated machine learning is unpacked. This book is for intermediate-level data scientists and machine learning engineers who want to learn how to apply key big data frameworks and ML and DL frameworks. You will need prior knowledge of the basics of statistics, Python programming, probability theories, and predictive analytics. What You Will LearnUnderstand widespread supervised and unsupervised learning, including key dimension reduction techniquesKnow the big data analytics layers such as data visualization, advanced statistics, predictive analytics, machine learning, and deep learningIntegrate big data frameworks with a hybrid of machine learning frameworks and deep learning frameworksDesign, build, test, and validate skilled machine models and deep learning modelsOptimize model performance using data transformation, regularization, outlier remedying, hyperparameter optimization, and data split ratio alteration Who This Book Is ForData scientists and machine learning engineers with basic knowledge and understanding of Python programming, probability theories, and predictive analytics

DKK 307.00
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Python for MATLAB Development - Albert Danial - Bog - APress - Plusbog.dk

Python for MATLAB Development - Albert Danial - Bog - APress - Plusbog.dk

MATLAB can run Python code! Python for MATLAB Development shows you how to enhance MATLAB with Python solutions to a vast array of computational problems in science, engineering, optimization, statistics, finance, and simulation. It is three books in one: - A thorough Python tutorial that leverages your existing MATLAB knowledge with a comprehensive collection of MATLAB/Python equivalent expressions - A reference guide to setting up and managing a Python environment that integrates cleanly with MATLAB - A collection of recipes that demonstrate Python solutions invoked directly from MATLAB This book shows how to call Python functions to enhance MATLAB''s capabilities. Specifically, you''ll see how Python helps MATLAB: - Run faster with numba - Distribute work to a compute cluster with dask - Find symbolic solutions to integrals, derivatives, and series summations with SymPy - Overlay data on maps with Cartopy - Solve mixed-integer linear programming problems with PuLP - Interact with Redis via pyredis, PostgreSQL via psycopg2, and MongoDB via pymongo - Read and write file formats that are not natively understood by MATLAB, such as SQLite, YAML, and ini Who This Book Is For MATLAB developers who are new to Python and other developers with some prior experience with MATLAB, R, IDL, or Mathematica.

DKK 519.00
1

Applied Deep Learning with TensorFlow 2 - Umberto Michelucci - Bog - APress - Plusbog.dk

Applied Deep Learning with TensorFlow 2 - Umberto Michelucci - Bog - APress - Plusbog.dk

Understand how neural networks work and learn how to implement them using TensorFlow 2.0 and Keras. This new edition focuses on the fundamental concepts and at the same time on practical aspects of implementing neural networks and deep learning for your research projects. This book is designed so that you can focus on the parts you are interested in. You will explore topics as regularization, optimizers, optimization, metric analysis, and hyper-parameter tuning. In addition, you will learn the fundamentals ideas behind autoencoders and generative adversarial networks. All the code presented in the book will be available in the form of Jupyter notebooks which would allow you to try out all examples and extend them in interesting ways. A companion online book is available with the complete code for all examples discussed in the book and additional material more related to TensorFlow and Keras. All the code will be available in Jupyter notebook format and can be opened directly in Google Colab (no need to install anything locally) or downloaded on your own machine and tested locally. You will: •Understand the fundamental concepts of how neural networks work •Learn the fundamental ideas behind autoencoders and generative adversarial networks •Be able to try all the examples with complete code examples that you can expand for your own projects •Have available a complete online companion book with examples and tutorials. This book is for: Readers with an intermediate understanding of machine learning, linear algebra, calculus, and basic Python programming.

DKK 495.00
1

Beginning Ada Programming - Andrew T. Shvets - Bog - APress - Plusbog.dk

Beginning Ada Programming - Andrew T. Shvets - Bog - APress - Plusbog.dk

Discover the Ada programming language by being gently guided through the various parts of the language and its latest available stable release. The goal in this book is to slowly ease you into the different topics. It is understood that you do not always have ample free time, so the text is easy to digest and concepts are spoon fed to the reader. Starting with the simplest of topics, detailed explanations demonstrate the how and why of Ada. You are strongly encouraged to experiment and break things (without which the learning process is linear and quite dull). At the end of Beginning Ada Programming , you will have an excellent understanding of the general topics that make up the Ada programming language and can tackle far more challenging topics. Each chapter builds on what was previously described. Furthermore, each code example is independent of others and will run all by itself. Instructions are provided where you can obtain an Ada compiler and how to debug your code. What You Will Learn - Master basic types, control structures, procedures, and functions in Ada - Use Ada arrays, records, and access types - Implement OO programming using Ada - Handle the basics of I/O and interfacing with the operating system - Take advantage of string operators, data containers, multiprocessing with tasks, and more - Work with contracts and proofs, networks, and various Ada libraries - Who This Book Is For Programmers who are new to Ada, with at least some experience in programming, especially scientific programming.

DKK 476.00
1

Practical Business Analytics Using R and Python - Umesh R. Hodeghatta - Bog - APress - Plusbog.dk

Practical Business Analytics Using R and Python - Umesh R. Hodeghatta - Bog - APress - Plusbog.dk

This book illustrates how data can be useful in solving business problems. It explores various analytics techniques for using data to discover hidden patterns and relationships, predict future outcomes, optimize efficiency and improve the performance of organizations. You''ll learn how to analyze data by applying concepts of statistics, probability theory, and linear algebra. In this new edition, both R and Python are used to demonstrate these analyses. Practical Business Analytics Using R and Python also features new chapters covering databases, SQL, Neural networks, Text Analytics, and Natural Language Processing. Part one begins with an introduction to analytics, the foundations required to perform data analytics, and explains different analytics terms and concepts such as databases and SQL, basic statistics, probability theory, and data exploration. Part two introduces predictive models using statistical machine learning and discusses concepts like regression, classification, and neural networks. Part three covers two of the most popular unsupervised learning techniques, clustering and association mining, as well as text mining and natural language processing (NLP). The book concludes with an overview of big data analytics, R and Python essentials for analytics including libraries such as pandas and NumPy. Upon completing this book, you will understand how to improve business outcomes by leveraging R and Python for data analytics. What You Will Learn - Master the mathematical foundations required for business analytics - Understand various analytics models and data mining techniques such as regression, supervised machine learning algorithms for modeling, unsupervised modeling techniques, and how to choose the correct algorithm for analysis in any given task - Use R and Python to develop descriptive models, predictive models, and optimize models - Interpret and recommend actions based on analytical model outcomes Who This Book Is For Software professionals and developers, managers, and executives who want to understand and learn the fundamentals of analytics using R and Python.

DKK 519.00
1

Options and Derivatives Programming in C++20 - Carlos Oliveira - Bog - APress - Plusbog.dk

Options and Derivatives Programming in C++20 - Carlos Oliveira - Bog - APress - Plusbog.dk

Master the features of C++ that are frequently used to write financial software for options and derivatives, including the STL, templates, functional programming, and numerical libraries. This book also covers new features introduced in C++20 and other recent standard releases: modules, concepts, spaceship operators, and smart pointers. You will explore how-to examples covering all the major tools and concepts used to build working solutions for quantitative finance. These include advanced C++ concepts as well as the basic building libraries used by modern C++ developers, such as the STL and Boost, while also leveraging knowledge of object-oriented and template-based programming. Options and Derivatives Programming in C++ provides a great value for readers who are trying to use their current programming knowledge in order to become proficient in the style of programming used in large banks, hedge funds, and other investment institutions. The topics covered in the book are introduced in a logical and structured way and even novice programmers will be able to absorb the most important topics and competencies. This book is written with the goal of reaching readers who need a concise, algorithms-based book, providing basic information through well-targeted examples and ready-to-use solutions. You will be able to directly apply the concepts and sample code to some of the most common problems faced in the analysis of options and derivative contracts. What You Will Learn - - Discover how C++ is used in the development of solutions for options and derivatives trading in the financial industry - Grasp the fundamental problems in options and derivatives trading - Converse intelligently about credit default swaps, Forex derivatives, and more - Implement valuation models and trading strategies - Build pricing algorithms around the Black-Sholes model, and also using the binomial and differential equations methods - Run quantitative finance algorithms using linear algebra techniques - Recognize and apply the most common design patterns used in options trading - Who This Book Is For Professional developers who have some experience with the C++ language and would like to leverage that knowledge into financial software development.

DKK 495.00
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The Art of Reinforcement Learning - Michael Hu - Bog - APress - Plusbog.dk

The Art of Reinforcement Learning - Michael Hu - Bog - APress - Plusbog.dk

Unlock the full potential of reinforcement learning (RL), a crucial subfield of Artificial Intelligence, with this comprehensive guide. This book provides a deep dive into RL's core concepts, mathematics, and practical algorithms, helping you to develop a thorough understanding of this cutting-edge technology. Beginning with an overview of fundamental concepts such as Markov decision processes, dynamic programming, Monte Carlo methods, and temporal difference learning, this book uses clear and concise examples to explain the basics of RL theory. The following section covers value function approximation, a critical technique in RL, and explores various policy approximations such as policy gradient methods and advanced algorithms like Proximal Policy Optimization (PPO). This book also delves into advanced topics, including distributed reinforcement learning, curiosity-driven exploration, and the famous AlphaZero algorithm, providing readers with a detailed account of these cutting-edge techniques. With a focus on explaining algorithms and the intuition behind them, The Art of Reinforcement Learning includes practical source code examples that you can use to implement RL algorithms. Upon completing this book, you will have a deep understanding of the concepts, mathematics, and algorithms behind reinforcement learning, making it an essential resource for AI practitioners, researchers, and students. What You Will LearnGrasp fundamental concepts and distinguishing features of reinforcement learning, including how it differs from other AI and non-interactive machine learning approachesModel problems as Markov decision processes, and how to evaluate and optimize policies using dynamic programming, Monte Carlo methods, and temporal difference learningUtilize techniques for approximating value functions and policies, including linear and nonlinear value function approximation and policy gradient methodsUnderstand the architecture and advantages of distributed reinforcement learningMaster the concept of curiosity-driven exploration and how it can be leveraged to improve reinforcement learning agentsExplore the AlphaZero algorithm and how it was able to beat professional Go players Who This Book Is ForMachine learning engineers, data scientists, software engineers, and developers who want to incorporate reinforcement learning algorithms into their projects and applications.

DKK 519.00
1

Learn Data Science Using SAS Studio - Engy Fouda - Bog - APress - Plusbog.dk

Learn Data Science Using SAS Studio - Engy Fouda - Bog - APress - Plusbog.dk

Do you want to create data analysis reports without writing a line of code? This book introduces SAS Studio, a free data science web browser-based product for educational and non-commercial purposes. The power of SAS Studio comes from its visual point-and-click user interface that generates SAS code. It is easier to learn SAS Studio than to learn R and Python to accomplish data cleaning, statistics, and visualization tasks. The book includes a case study about analyzing the data required for predicting the results of presidential elections in the state of Maine for 2016 and 2020. In addition to the presidential elections, the book provides real-life examples including analyzing stocks, oil and gold prices, crime, marketing, and healthcare. You will see data science in action and how easy it is to perform complicated tasks and visualizations in SAS Studio. You will learn, step-by-step, how to do visualizations, including maps. In most cases, you will not need a line of code as you work with the SAS Studio graphical user interface. The book includes explanations of the code that SAS Studio generates automatically. You will learn how to edit this code to perform more complicated advanced tasks. The book introduces you to multiple SAS products such as SAS Viya, SAS Analytics, and SAS Visual Statistics. What You Will Learn - Become familiar with SAS Studio IDE - Understand essential visualizations - Know the fundamental statistical analysis required in most data science and analytics reports - Clean the most common data set problems - Use linear progression for data prediction - Write programs in SAS - Get introduced to SAS-Viya, which is more potent than SAS studio Who This Book Is For A general audience of people who are new to data science, students, and data analysts and scientists who are experienced but new to SAS. No programming or in-depth statistics knowledge is needed.

DKK 476.00
1

Econometrics and Data Science - Tshepo Chris Nokeri - Bog - APress - Plusbog.dk

Econometrics and Data Science - Tshepo Chris Nokeri - Bog - APress - Plusbog.dk

Get up to speed on the application of machine learning approaches in macroeconomic research. This book brings together economics and data science. Author Tshepo Chris Nokeri begins by introducing you to covariance analysis, correlation analysis, cross-validation, hyperparameter optimization, regression analysis, and residual analysis. In addition, he presents an approach to contend with multi-collinearity. He then debunks a time series model recognized as the additive model. He reveals a technique for binarizing an economic feature to perform classification analysis using logistic regression. He brings in the Hidden Markov Model, used to discover hidden patterns and growth in the world economy. The author demonstrates unsupervised machine learning techniques such as principal component analysis and cluster analysis. Key deep learning concepts and ways of structuring artificial neural networks are explored along with training them and assessing their performance. The Monte Carlo simulation technique is applied to stimulate the purchasing power of money in an economy. Lastly, the Structural Equation Model (SEM) is considered to integrate correlation analysis, factor analysis, multivariate analysis, causal analysis, and path analysis. After reading this book, you should be able to recognize the connection between econometrics and data science. You will know how to apply a machine learning approach to modeling complex economic problems and others beyond this book. You will know how to circumvent and enhance model performance, together with the practical implications of a machine learning approach in econometrics, and you will be able to deal with pressing economic problems. What You Will Learn - Examine complex, multivariate, linear-causal structures through the path and structural analysis technique, including non-linearity and hidden states - Be familiar with practical applications of machine learning and deep learning in econometrics - Understand theoretical framework and hypothesis development, and techniques for selecting appropriate models - Develop, test, validate, and improve key supervised (i.e., regression and classification) and unsupervised (i.e., dimension reduction and cluster analysis) machine learning models, alongside neural networks, Markov, and SEM models - Represent and interpret data and models Who This Book Is For Beginning and intermediate data scientists, economists, machine learning engineers, statisticians, and business executives

DKK 294.00
1

Industrial Robotics Control - Fabrizio Frigeni - Bog - APress - Plusbog.dk

Industrial Robotics Control - Fabrizio Frigeni - Bog - APress - Plusbog.dk

Build a complete control system for industrial robots, learning all the theory and practical tips from the perspective of an automation engineer. Explore the details of kinematics, trajectories, and motion control, and then create your own circuit board to drive the electric motors and move the robot. After covering the theory, readers can put what they''ve learned in practice by programming a control firmware for the robot. Each software component is described in detail, from the HMI and the interpreter of motion commands, to the servo loop controller at the core of each servo drive. In particular, the author presents the commutation algorithm and the servo loop controller for brushless synchronous motors, which are typically employed in robotics applications. Readers will also learn how to calibrate the robot, commission it to the end-user, and design a digital twin to test and monitor the entire workcell in a safe simulated environment. Finally, the book delves into hardware, covering how to select and use electric motors and encoders, how to build servo drives and motion controllers, and how to design your own PCBs. Different electronic components and their application circuits are analyzed, showing the advantages and drawbacks of each. By the end of the book you should be able to design and build electronic boards and write their core firmware to control any kind of industrial robot for all sorts of different practical applications. What you''ll learn - Solve kinematics models of robots - Generate safe paths and optimal motion trajectories - Create a digital twin of your robot to test and monitor its movements - Master the electronic commutation and closed-loop control of brushless motors - Design electronics circuit boards for motion applications Who This Book Is For Robotics engineers (and students) who want to understand the theory behind the control of robotics arms, from the kinematic models of their axes to the electronic commutation of their motors. Some basic calculus and linear algebra is required for the understanding of the geometrical framework, while some electronics foundations are helpful to grasp the details of the circuits design.

DKK 385.00
1

Target C# - Gerard Byrne - Bog - APress - Plusbog.dk

Target C# - Gerard Byrne - Bog - APress - Plusbog.dk

So, you want to learn C# and Visual Studio 2022, but are a bit intimidated? Don''t be. Programming is within your grasp! Programmers at any level have to fully understand, and more importantly, be able to code the core constructs. It is impossible to use complex programming concepts such as classes before understanding what methods and variables and their data types are. Once there is a foundation built on the basics, then all other topics can fall in line. While it is a forgone conclusion that languages change with the introduction of new features, the core concepts do not. Even large enterprises do not always update to the latest versions of languages and frameworks; their "backbone" applications have been developed to work, regardless. More than ever, enterprises need developers who can master and apply the core programming concepts and then be "up-skilled" with newer language levels and features as they integrate into the company. This book builds from the ground up. You will begin with an introduction to programming, learning the foundational concepts needed to become a C# programmer. You will then put to practice a wide range of programming concepts, including data types, selection, iteration, arrays, methods, classes and objects, serialization, file handling, and string handling. You will learn enough to develop applications that emulate commercial application code. Once you''ve got the foundational concepts, get ready to dive into common programming routines, including linear search, binary search, bubble sort and insertion sort, and use C# to code them. Code example annotations supplement the learning and are designed to enhance learning while also explaining why the code does what it does. This book: - Teaches core programming through well-explained and simple-to-follow instructions - Reinforces programming skills through the use of coding examples that extend user learnings - Explains theoretical programming concepts; applies them practically with code examples - Introduces the latest Microsoft C# Integrated Development Environment (Visual Studio 2022) - Enlists clear, precise, and easy-to-understand language to assist readers of all levels and experience - Uses a mix of "theory" and practical information that is designed to be friendly and engaging Who This Book Is For Beginners, those refreshing their C# skills, or those moving from another programming language. No skills or previous knowledge is required. Readers will need to download Visual Studio 2022 Community Edition as this is what the book code has been based on, but they could use other Integrated Development Environments.

DKK 519.00
1

Introducing Microsoft Quantum Computing for Developers - Johnny Hooyberghs - Bog - APress - Plusbog.dk

Introducing Microsoft Quantum Computing for Developers - Johnny Hooyberghs - Bog - APress - Plusbog.dk

Dive in with this hands-on introduction to quantum computing with the Microsoft Quantum Development Kit and Q# for software developers. You may have heard about quantum computing, but what does it mean to you as a software developer? With many new developments, a resurgence in interest, and investment by some of the largest tech companies in the world to be the first to market with quantum programming (QP) hardware and platforms, it is no longer a tool in the distant future. Developers are at the forefront, now able to create applications that take advantage of QP through simulations. While the skill is of interest, for many developers, quantum computing and its implications still remains a mystery. In this book, you will get up to speed exploring important quantum concepts and apply them in practice through writing actual quantum algorithms, using the Microsoft Quantum Development Kit. Theoretical knowledge about quantum physics, such as superposition and entanglement, will be used to explain quantum computing topics, including quantum gates, quantum circuits, and quantum algorithms. Finally, take a tour of the new Azure Quantum. Use Q#, Microsoft's new programming language, to target quantum hardware. You will select your supporting language of choice, either C# or Python, to begin writing your quantum applications. Combined with just enough theoretical preparation, you will learn how to get your computer ready to simulate basic quantum programs using Microsoft Visual Studio or Visual Studio Code and Q#. What You Will LearnGet up to speed on the platform-independent quantum tool set using the Microsoft Quantum Development Kit simulator and Visual Studio Code or Microsoft Visual Studio Know the basics of quantum mechanics required to start working on quantum computing Understand mathematical concepts such as complex numbers, trigonometry, and linear algebraInstall the Microsoft Quantum Development Kit on a Windows or Linux PC with Visual Studio Code or Microsoft Visual Studio Write quantum algorithms with the Microsoft Quantum Development Kit and Q#, supported by C# or Python Discover insights on important existing quantum algorithms such as Deutch, Deutch-Jozsa, and the fun CHSH-gameGet introduced to quantum as a service using the Microsoft Azure Quantum preview cloud offering Who This Book Is ForDevelopers who are interested in quantum computing, specifically those software developers who are planning on using quantum computers in the future. Basic imperative programming knowledge is useful to understand the syntax and structure found in the Q# programming language. Knowledge of Microsoft C# or Python is not required since these languages are only used to support the simulation of Q# on a classical computer.

DKK 495.00
1

Beginning x64 Assembly Programming - Jo Van Hoey - Bog - APress - Plusbog.dk

Beginning x64 Assembly Programming - Jo Van Hoey - Bog - APress - Plusbog.dk

Program in assembly starting with simple and basic programs, all the way up to AVX programming. By the end of this book, you will be able to write and read assembly code, mix assembly with higher level languages, know what AVX is, and a lot more than that. The code used in Beginning x64 Assembly Programming is kept as simple as possible, which means: no graphical user interfaces or whistles and bells or error checking. Adding all these nice features would distract your attention from the purpose: learning assembly language. The theory is limited to a strict minimum: a little bit on binary numbers, a short presentation of logical operators, and some limited linear algebra. And we stay far away from doing floating point conversions. The assembly code is presented in complete programs, so that you can test them on your computer, play with them, change them, break them. This book will also show you what tools can be used, how to use them, and the potential problems in those tools. It is not the intention to give you a comprehensive course on all of the assembly instructions, which is impossible in one book: look at the size of the Intel Manuals. Instead, the author will give you a taste of the main items, so that you will have an idea about what is going on. If you work through this book, you will acquire the knowledge to investigate certain domains more in detail on your own. The majority of the book is dedicated to assembly on Linux, because it is the easiest platform to learn assembly language. At the end the author provides a number of chapters to get you on your way with assembly on Windows. You will see that once you have Linux assembly under your belt, it is much easier to take on Windows assembly. This book should not be the first book you read on programming, if you have never programmed before, put this book aside for a while and learn some basics of programming with a higher-level language such as C. What You Will Learn - Discover how a CPU and memory works - Appreciate how a computer and operating system work together - See how high-level language compilers generate machine language, and use that knowledge to write more efficient code - Be better equipped to analyze bugs in your programs - Get your program working, which is the fun part - Investigate malware and take the necessary actions and precautions Who This Book Is For Programmers in high level languages. It is also for systems engineers and security engineers working for malware investigators. Required knowledge: Linux, Windows, virtualization, and higher level programming languages (preferably C or C++).

DKK 391.00
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