Based on the practical experiences of its authors, who collectively have spent several decades teaching software skills to scientists. Readers only need a basic understanding of Python includes over a hundred exercises to allow readers to practice their skills

This easy-to-understand textbook presents a modern approach to learning numerical methods (or scientific computing), with a unique focus on the modeling and applications of the mathematical content. Emphasis is placed on the need for, and methods of, scientific computing for a range of different types of problems, supplying the evidence and justification to motivate the reader. Practical guidance on coding the methods is also provided, through simple-to-follow examples using Python. Topics and features: provides an accessible and applications-oriented approach, supported by working Python code for many of the methods; encourages both problem- and project-based learning through extensive examples, exercises, and projects drawn from practical applications; introduces the main concepts in modeling, python programming, number representation, and errors; explains the essential details of numerical calculus, linear, and nonlinear equations, including the multivariable Newton method; discusses interpolation and the numerical solution of differential equations, covering polynomial interpolation, splines, and the Euler, Runge-Kutta, and shooting methods; presents largely self-contained chapters, arranged in a logical order suitable for an introductory course on scientific computing. Undergraduate students embarking on a first course on numerical methods or scientific computing will find this textbook to be an invaluable guide to the field, and to the application of these methods across such varied disciplines as computer science, engineering, mathematics, economics, the physical sciences, and social science.

This book introduces Python scripting for geographic information science (GIS) workflow optimization using ArcGIS. It builds essential programming skills for automating GIS analysis. Over 200 sample Python scripts and 175 classroom-tested exercises reinforce the learning objectives. Readers will learn to: * Write and run Python in the ArcGIS Python Window, the PythonWin IDE, and the PyScripter IDE * Work with Python syntax and data types * Call ArcToolbox tools, batch process GIS datasets, and manipulate map documents using the arcpy package * Read and modify proprietary and ASCII text GIS data * Parse HTML web pages and KML datasets * Create Web pages and fetch GIS data from Web sources. * Build user-interfaces with the native Python file dialog toolkit or the ArcGIS Script tools and PyToolboxes Python for ArcGIS is designed as a primary textbook for advanced-level students in GIS. Researchers, government specialists and professionals working in GIS will also find this book useful as a reference.

Recent progress in artificial intelligence (AI) has revolutionized our everyday life. Many AI algorithms have reached human-level performance and AI agents are replacing humans in most professions. It is predicted that this trend will continue and 30% of work activities in 60% of current occupations will be automated. This success, however, is conditioned on availability of huge annotated datasets to training AI models. Data annotation is a time-consuming and expensive task which still is being performed by human workers. Learning efficiently from less data is a next step for making AI more similar to natural intelligence. Transfer learning has been suggested a remedy to relax the need for data annotation. The core idea in transfer learning is to transfer knowledge across similar tasks and use similarities and previously learned knowledge to learn more efficiently. In this book, we provide a brief background on transfer learning and then focus on the idea of transferring knowledge through intermediate embedding spaces. The idea is to couple and relate different learning through embedding spaces that encode task-level relations and similarities. We cover various machine learning scenarios and demonstrate that this idea can be used to overcome challenges of zero-shot learning, few-shot learning, domain adaptation, continual learning, lifelong learning, and collaborative learning.

Newly updated with over 150 pages of material on the latest Tcl extensions, "Tcl/Tk: A Developer s Gu"ide is a unique practical tutorial for professional programmers and beginners alike. Starting with a clear picture of the basics, Tcl/Tk covers the variety of tools in this "Swiss army knife" of programming languages, giving you the ability to enhance your programs, extend your application's capabilities, and become a more effective programmer.

This updated edition covers all of the new features of version 8.6, including object-oriented programming and the creation of megawidgets, existing data structure implementations, themed widgets and virtual events. Extensive code snippets and online tutorials in various languages will give you a firm grasp on how to use the Tcl/Tk libraries and interpreters and, most importantly, on what constitutes an effective strategy for using Tcl/Tk.
Includes the latest features of Tcl/Tk 8.6Covers Tcl development tools, popular extensions, and packages to allow developers to solve real-world problems with Tcl/Tk immediatelyProvides straightforward explanations for beginners and offers tips, style guidelines, and debugging techniques for advanced usersCompanion website includes Tcl/Tk tutorials, applications, distributions, and more tools"

Research and Evidence in Software Engineering: From Empirical Studies to Open Source Artifacts introduces advanced software engineering to software engineers, scientists, postdoctoral researchers, academicians, software consultants, management executives, doctoral students, and advanced level postgraduate computer science students. This book contains research articles addressing numerous software engineering research challenges associated with various software development-related activities, including programming, testing, measurements, human factors (social software engineering), specification, quality, program analysis, software project management, and more. It provides relevant theoretical frameworks, empirical research findings, and evaluated solutions addressing the research challenges associated with the above-mentioned software engineering activities. To foster collaboration among the software engineering research community, this book also reports datasets acquired systematically through scientific methods and related to various software engineering aspects that are valuable to the research community. These datasets will allow other researchers to use them in their research, thus improving the quality of overall research. The knowledge disseminated by the research studies contained in the book will hopefully motivate other researchers to further innovation in the way software development happens in real practice.

Driven by concrete computational problems in quantitative finance, this book provides aspiring quant developers with the numerical techniques and programming skills they need. The authors start from scratch, so the reader does not need any previous experience of C++. Beginning with straightforward option pricing on binomial trees, the book gradually progresses towards more advanced topics, including nonlinear solvers, Monte Carlo techniques for path-dependent derivative securities, finite difference methods for partial differential equations, and American option pricing by solving a linear complementarity problem. Further material, including solutions to all exercises and C++ code, is available online. The book is ideal preparation for work as an entry-level quant programmer and it gives readers the confidence to progress to more advanced skill sets involving C++ design patterns as applied in finance.

Your research has generated gigabytes of data and now you need to analyse it. You hate using spreadsheets but it is all you know, so what else can you do? This book will transform how you work with large and complex data sets, teaching you powerful programming tools for slicing and dicing data to suit your needs. Written in a fun and accessible style, this step-by-step guide will inspire and inform non-programmers about the essential aspects of Unix and Perl. It shows how, with just a little programming knowledge, you can write programs that could save you hours, or even days. No prior experience is required and new concepts are introduced using numerous code examples that you can try out for yourself. Going beyond the basics, the authors touch upon many broader topics that will help those new to programming, including debugging and how to write in a good programming style.

In ancient games such as chess or go, the most brilliant players can improve by studying the strategies produced by a machine. Robotic systems practice their own movements. In arcade games, agents capable of learning reach superhuman levels within a few hours. How do these spectacular reinforcement learning algorithms work? With easy-to-understand explanations and clear examples in Java and Greenfoot, you can acquire the principles of reinforcement learning and apply them in your own intelligent agents. Greenfoot (M.Koelling, King's College London) and the hamster model (D. Bohles, University of Oldenburg) are simple but also powerful didactic tools that were developed to convey basic programming concepts. The result is an accessible introduction into machine learning that concentrates on reinforcement learning. Taking the reader through the steps of developing intelligent agents, from the very basics to advanced aspects, touching on a variety of machine learning algorithms along the way, one is allowed to play along, experiment, and add their own ideas and experiments.

Why learn functional programming? Isn't that some complicated ivory-tower technique used only in obscure languages like Haskell? In fact, functional programming is actually very simple. It's also very powerful, as Haskell demonstrates by throwing away all the conventional programming tools and using only functional programming features. But it doesn't have to be done that way. Functional programming is a power tool that you can use in addition to all your usual tools, to whatever extent your current mainstream language supports it. Most languages have at least basic support. In this book we use Python and Java and, as a bonus, Scala. If you prefer another language, there will be minor differences in syntax, but the concepts are the same. Give functional programming a try. You may be surprised how much a single power tool can help you in your day-to-day programming.

From the descriptive styles which mirror 19th Century Romanticism to the bold starkness of contemporary dialects, these stories offer myriad insights into the many facets of the masculine soul. These are not cat stories. Well.actually there is one. But for the most part this is a collection of first person narratives in short fiction which are certain to leave the reader begging to dialogue with every page.

As with his powerful and imaginative science fiction novels, "The Last Champion of Earth" and "The Planet of Mortal Worship," Donald Templeman entertains, stuns and unveils lurid insights into the human condition with every keystroke.

'Author Templeman is a skilled writer and accomplished storyteller"-WRITER'S DIGEST 12TH ANNUAL BOOK AWARDS

Induction is a pervasive tool in computer science and mathematics for defining objects and reasoning on them. Coinduction is the dual of induction and as such it brings in quite different tools. Today, it is widely used in computer science, but also in other fields, including artificial intelligence, cognitive science, mathematics, modal logics, philosophy and physics. The best known instance of coinduction is bisimulation, mainly employed to define and prove equalities among potentially infinite objects: processes, streams, non-well-founded sets, etc. This book presents bisimulation and coinduction: the fundamental concepts and techniques and the duality with induction. Each chapter contains exercises and selected solutions, enabling students to connect theory with practice. A special emphasis is placed on bisimulation as a behavioural equivalence for processes. Thus the book serves as an introduction to models for expressing processes (such as process calculi) and to the associated techniques of operational and algebraic analysis.

Coinduction is a method for specifying and reasoning about infinite data types and automata with infinite behaviour. In recent years, it has come to play an ever more important role in the theory of computing. It is studied in many disciplines, including process theory and concurrency, modal logic and automata theory. Typically, coinductive proofs demonstrate the equivalence of two objects by constructing a suitable bisimulation relation between them. This collection of surveys is aimed at both researchers and Master's students in computer science and mathematics and deals with various aspects of bisimulation and coinduction, with an emphasis on process theory. Seven chapters cover the following topics: history, algebra and coalgebra, algorithmics, logic, higher-order languages, enhancements of the bisimulation proof method, and probabilities. Exercises are also included to help the reader master new material.

Python for Scientific Computation and Artificial Intelligence is split into 3 parts: in Section 1, the reader is introduced to the Python programming language and shown how Python can aid in the understanding of advanced High School Mathematics. In Section 2, the reader is shown how Python can be used to solve real-world problems from a broad range of scientific disciplines. Finally, in Section 3, the reader is introduced to neural networks and shown how TensorFlow (written in Python) can be used to solve a large array of problems in Artificial Intelligence (AI). This book was developed from a series of national and international workshops that the author has been delivering for over twenty years. The book is beginner friendly and has a strong practical emphasis on programming and computational modelling. Features: No prior experience of programming is required. Online GitHub repository available with codes for readers to practice. Covers applications and examples from biology, chemistry, computer science, data science, electrical and mechanical engineering, economics, mathematics, physics, statistics and binary oscillator computing. Full solutions to exercises are available as Jupyter notebooks on the Web.

This textbook is an ideal introduction in college courses or self-study for learning computer programming using the C language. Written for those with minimal or no programming experience, Computer Programming in C for Beginners offers a heavily guided, hands-on approach that enables the reader to quickly start programming, and then progresses to cover the major concepts of C programming that are critical for an early stage programmer to know and understand. While the progression of topics is conventional, their treatment is innovative and designed for rapid understanding of the many concepts in C that have traditionally proven difficult for beginners, such as variable typing and scope, function definition, passing by value, pointers, passing by reference, arrays, structures, basic memory management, dynamic memory allocation, and linked lists, as well as an introductory treatment of searching and sorting algorithms. Written in an informal but clear narrative, the book uses extensive examples throughout and provides detailed guidance on how to write the C code to achieve the objectives of the example problems. Derived from the author's many years of teaching hands-on college courses, it encourages the reader to follow along by programming the progressively more complex exercise programs presented. In some sections, errors are purposely inserted into the code to teach the reader about the common pitfalls of programming in general, and the C language in particular.

This book presents a number of approaches to Fine-Kinney-based multi-criteria occupational risk-assessment. For each proposed approach, it provides case studies demonstrating their applicability, as well as Python coding, which will enable readers to implement them into their own risk assessment process. The book begins by giving a review of Fine-Kinney occupational risk-assessment methods and their extension by fuzzy sets. It then progresses in a logical fashion, dedicating a chapter to each approach, including the fuzzy best and worst method, interval-valued Pythagorean fuzzy VIKOR and interval type-2 fuzzy QUALIFLEX. This book will be of interest to professionals and researchers working in the field of occupational risk management, as well as postgraduate and undergraduate students studying applications of fuzzy systems.

More than ever, FDL is the place for researchers, developers, industry designers, academia, and EDA tool companies to present and to learn about the latest scientific achievements, practical applications and users experiences in the domain of specification and design languages. FDL covers the modeling and design methods, and their latest supporting tools, for complex embedded systems, systems on chip, and heterogeneous systems.

FDL 2009 is the twelfth in a series of events that were held all over Europe, in selected locations renowned for their Universities and Reseach Institutions as well as the importance of their industrial environment in Computer Science and Micro-electronics. In 2009, FDL was organized in the attractive south of France area of Sophia Antipolis. together with the DASIP (Design and Architectures for Signal and Image Processing) Conference and the SAME (Sophia Antipolis MicroElectronics ) Forum.

All submitted papers were carefully reviewed to build a program with 27 full and 10 short contributions. From these, the Program Committee selected a shorter list, based on the evaluations of the reviewers, and the originality and relevance of the work that was presented at the Forum. The revised, and sometimes extended versions of these contributions constitute the chapters of this volume.

Advances in Design Methods from Modeling Languages for Embedded Systems and SoC's presents extensions to standard specification and description languages, as well as new language-based design techniques and methodologies to solve the challenges raised by mixed signal and multi-processor systems on a chip. It is intended as a reference for researchers and lecturers, as well as a state of the art milestone for designers and CAD developers.

Introduction to Data Compression, Fifth Edition, builds on the success of what is widely considered the best introduction and reference text on the art and science of data compression. Data compression techniques and technology are ever-evolving with new applications in image, speech, text, audio and video. This new edition includes all the latest developments in the field. Khalid Sayood provides an extensive introduction to the theory underlying today's compression techniques, with detailed instruction for their applications using several examples to explain the concepts. Encompassing the entire field of data compression, the book includes lossless and lossy compression, Huffman coding, arithmetic coding, dictionary techniques, context based compression, and scalar and vector quantization. The book provides a comprehensive working knowledge of data compression, giving the reader the tools to develop a complete and concise compression package.

Based on the author's experience in teaching data science for more than 10 years, Mathematics and Programming for Machine Learning with R: From the Ground Up reveals how machine learning algorithms do their magic and explains how these algorithms can be implemented in code. It is designed to provide readers with an understanding of the reasoning behind machine learning algorithms as well as how to program them. Written for novice programmers, the book progresses step-by-step, providing the coding skills needed to implement machine learning algorithms in R. The book begins with simple implementations and fundamental concepts of logic, sets, and probability before moving to the coverage of powerful deep learning algorithms. The first eight chapters deal with probability-based machine learning algorithms, and the last eight chapters deal with machine learning based on artificial neural networks. The first half of the book does not require mathematical sophistication, although familiarity with probability and statistics would be helpful. The second half assumes the reader is familiar with at least one semester of calculus. The text guides novice R programmers through algorithms and their application and along the way; the reader gains programming confidence in tackling advanced R programming challenges. Highlights of the book include: More than 400 exercises A strong emphasis on improving programming skills and guiding beginners to the implementation of full-fledged algorithms Coverage of fundamental computer and mathematical concepts including logic, sets, and probability In-depth explanations of machine learning algorithms

As natural language processing spans many different disciplines, it is sometimes difficult to understand the contributions and the challenges that each of them presents. This book explores the special relationship between natural language processing and cognitive science, and the contribution of computer science to these two fields. It is based on the recent research papers submitted at the international workshops of Natural Language and Cognitive Science (NLPCS) which was launched in 2004 in an effort to bring together natural language researchers, computer scientists, and cognitive and linguistic scientists to collaborate together and advance research in natural language processing. The chapters cover areas related to language understanding, language generation, word association, word sense disambiguation, word predictability, text production and authorship attribution. This book will be relevant to students and researchers interested in the interdisciplinary nature of language processing.

Tackles a topic in a concise and accessible way that most believe too advanced to pick up easily. Author has over 40 years teaching and industry experience which they utilize in this book. Contains an appendix with extended code and examples of topics discussed in text.

Digital Image Processing with C++ presents the theory of digital image processing, and implementations of algorithms using a dedicated library. Processing a digital image means transforming its content (denoising, stylizing, etc.), or extracting information to solve a given problem (object recognition, measurement, motion estimation, etc.). This book presents the mathematical theories underlying digital image processing, as well as their practical implementation through examples of algorithms implemented in the C++ language, using the free and easy-to-use CImg library. Chapters cover in a broad way the field of digital image processing and proposes practical and functional implementations of each method theoretically described. The main topics covered include filtering in spatial and frequency domains, mathematical morphology, feature extraction and applications to segmentation, motion estimation, multispectral image processing and 3D visualization. Students or developers wishing to discover or specialize in this discipline, teachers and researchers wishing to quickly prototype new algorithms, or develop courses, will all find in this book material to discover image processing or deepen their knowledge in this field.

How to Reduce Code Complexity and Develop Software More Sustainably "Mark Seemann is well known for explaining complex concepts clearly and thoroughly. In this book he condenses his wide-ranging software development experience into a set of practical, pragmatic techniques for writing sustainable and human-friendly code. This book will be a must-read for every programmer." -- Scott Wlaschin, author of Domain Modeling Made Functional Code That Fits in Your Head offers indispensable, practical advice for writing code at a sustainable pace and controlling the complexity that causes projects to spin out of control. Reflecting decades of experience helping software teams succeed, Mark Seemann guides you from zero (no code) to deployed features and shows how to maintain a good cruising speed as you add functionality, address cross-cutting concerns, troubleshoot, and optimize. You'll find valuable ideas, practices, and processes for key issues ranging from checklists to teamwork, encapsulation to decomposition, API design to unit testing. Seemann illuminates his insights with code examples drawn from a complete sample project. Written in C#, they're designed to be clear and useful to anyone who uses any object-oriented language including Java , C++, and Python. To facilitate deeper exploration, all code and extensive commit messages are available for download. Choose mindsets and processes that work, and escape bad metaphors that don't Use checklists to liberate yourself, improving outcomes with the skills you already have Get past "analysis paralysis" by creating and deploying a vertical slice of your application Counteract forces that lead to code rot and unnecessary complexity Master better techniques for changing code behavior Discover ways to solve code problems more quickly and effectively Think more productively about performance and security If you've ever suffered through bad projects or had to cope with unmaintainable legacy code, this guide will help you make things better next time and every time. Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.