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| computer programming and analysis: Flow Analysis of Computer Programs Matthew S. Hecht, 1977 |
| computer programming and analysis: Principles of Program Analysis Flemming Nielson, Hanne R. Nielson, Chris Hankin, 2015-02-27 Program analysis utilizes static techniques for computing reliable information about the dynamic behavior of programs. Applications include compilers (for code improvement), software validation (for detecting errors) and transformations between data representation (for solving problems such as Y2K). This book is unique in providing an overview of the four major approaches to program analysis: data flow analysis, constraint-based analysis, abstract interpretation, and type and effect systems. The presentation illustrates the extensive similarities between the approaches, helping readers to choose the best one to utilize. |
| computer programming and analysis: Elements of Programming Alexander Stepanov, Paul McJones, 2019-06-17 Elements of Programming provides a different understanding of programming than is presented elsewhere. Its major premise is that practical programming, like other areas of science and engineering, must be based on a solid mathematical foundation. This book shows that algorithms implemented in a real programming language, such as C++, can operate in the most general mathematical setting. For example, the fast exponentiation algorithm is defined to work with any associative operation. Using abstract algorithms leads to efficient, reliable, secure, and economical software. |
| computer programming and analysis: Artificial Intelligence with Python Prateek Joshi, 2017-01-27 Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you About This Book Step into the amazing world of intelligent apps using this comprehensive guide Enter the world of Artificial Intelligence, explore it, and create your own applications Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no time Who This Book Is For This book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks. What You Will Learn Realize different classification and regression techniques Understand the concept of clustering and how to use it to automatically segment data See how to build an intelligent recommender system Understand logic programming and how to use it Build automatic speech recognition systems Understand the basics of heuristic search and genetic programming Develop games using Artificial Intelligence Learn how reinforcement learning works Discover how to build intelligent applications centered on images, text, and time series data See how to use deep learning algorithms and build applications based on it In Detail Artificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications. During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide! Style and approach This highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. In every chapter, we explain an algorithm, implement it, and then build a smart application. |
| computer programming and analysis: Analysis for Computer Scientists Michael Oberguggenberger, Alexander Ostermann, 2018-10-24 This easy-to-follow textbook/reference presents a concise introduction to mathematical analysis from an algorithmic point of view, with a particular focus on applications of analysis and aspects of mathematical modelling. The text describes the mathematical theory alongside the basic concepts and methods of numerical analysis, enriched by computer experiments using MATLAB, Python, Maple, and Java applets. This fully updated and expanded new edition also features an even greater number of programming exercises. Topics and features: describes the fundamental concepts in analysis, covering real and complex numbers, trigonometry, sequences and series, functions, derivatives, integrals, and curves; discusses important applications and advanced topics, such as fractals and L-systems, numerical integration, linear regression, and differential equations; presents tools from vector and matrix algebra in the appendices, together with further information on continuity; includes added material on hyperbolic functions, curves and surfaces in space, second-order differential equations, and the pendulum equation (NEW); contains experiments, exercises, definitions, and propositions throughout the text; supplies programming examples in Python, in addition to MATLAB (NEW); provides supplementary resources at an associated website, including Java applets, code source files, and links to interactive online learning material. Addressing the core needs of computer science students and researchers, this clearly written textbook is an essential resource for undergraduate-level courses on numerical analysis, and an ideal self-study tool for professionals seeking to enhance their analysis skills. |
| computer programming and analysis: Computer Programming and Numerical Analysis Revised Edition with C: A Integrated Approach N. Datta, 2003-10 The availability of high-speed digital computers has led to the widespread study of computer programming and numerical analysis in Indian universities and technological institutes. This book presents the theory and applications of numerical methods for the solution of various types of computational problems in science and engineering. |
| computer programming and analysis: Computer Analysis of Structures Siegfried M. Holzer, 1985 This textbook is designed to help engineering students acquire a precise understanding of the matrix development methods and its underlying concepts and principles, and to acquire experience in developing well-structured programs. A distinguishing feature of this class-tested textbook is its integrated instruction of structured programming and the matrix development method. Focusing on principles taught in sophomore and junior level courses, the book is intended for structural engineering students in civil engineering, aerospace engineering, mechanics, and related disciplines. |
| computer programming and analysis: An Introduction to the Analysis of Algorithms Robert Sedgewick, Philippe Flajolet, 2013-01-18 Despite growing interest, basic information on methods and models for mathematically analyzing algorithms has rarely been directly accessible to practitioners, researchers, or students. An Introduction to the Analysis of Algorithms, Second Edition, organizes and presents that knowledge, fully introducing primary techniques and results in the field. Robert Sedgewick and the late Philippe Flajolet have drawn from both classical mathematics and computer science, integrating discrete mathematics, elementary real analysis, combinatorics, algorithms, and data structures. They emphasize the mathematics needed to support scientific studies that can serve as the basis for predicting algorithm performance and for comparing different algorithms on the basis of performance. Techniques covered in the first half of the book include recurrences, generating functions, asymptotics, and analytic combinatorics. Structures studied in the second half of the book include permutations, trees, strings, tries, and mappings. Numerous examples are included throughout to illustrate applications to the analysis of algorithms that are playing a critical role in the evolution of our modern computational infrastructure. Improvements and additions in this new edition include Upgraded figures and code An all-new chapter introducing analytic combinatorics Simplified derivations via analytic combinatorics throughout The book’s thorough, self-contained coverage will help readers appreciate the field’s challenges, prepare them for advanced results—covered in their monograph Analytic Combinatorics and in Donald Knuth’s The Art of Computer Programming books—and provide the background they need to keep abreast of new research. [Sedgewick and Flajolet] are not only worldwide leaders of the field, they also are masters of exposition. I am sure that every serious computer scientist will find this book rewarding in many ways. —From the Foreword by Donald E. Knuth |
| computer programming and analysis: Linear Programming Michael J. Best, Klaus Ritter, 1985 |
| computer programming and analysis: Computer Programming with C++ Kunal Pimparkhede, 2017-01-16 Provides an in-depth explanation of the C and C++ programming languages along with the fundamentals of object oriented programming paradigm-- |
| computer programming and analysis: Introduction to Static Analysis Xavier Rival, Kwangkeun Yi, 2020-02-11 A self-contained introduction to abstract interpretation–based static analysis, an essential resource for students, developers, and users. Static program analysis, or static analysis, aims to discover semantic properties of programs without running them. It plays an important role in all phases of development, including verification of specifications and programs, the synthesis of optimized code, and the refactoring and maintenance of software applications. This book offers a self-contained introduction to static analysis, covering the basics of both theoretical foundations and practical considerations in the use of static analysis tools. By offering a quick and comprehensive introduction for nonspecialists, the book fills a notable gap in the literature, which until now has consisted largely of scientific articles on advanced topics. The text covers the mathematical foundations of static analysis, including semantics, semantic abstraction, and computation of program invariants; more advanced notions and techniques, including techniques for enhancing the cost-accuracy balance of analysis and abstractions for advanced programming features and answering a wide range of semantic questions; and techniques for implementing and using static analysis tools. It begins with background information and an intuitive and informal introduction to the main static analysis principles and techniques. It then formalizes the scientific foundations of program analysis techniques, considers practical aspects of implementation, and presents more advanced applications. The book can be used as a textbook in advanced undergraduate and graduate courses in static analysis and program verification, and as a reference for users, developers, and experts. |
| computer programming and analysis: Programming Computer Vision with Python Jan Erik Solem, 2012-06-19 If you want a basic understanding of computer vision’s underlying theory and algorithms, this hands-on introduction is the ideal place to start. You’ll learn techniques for object recognition, 3D reconstruction, stereo imaging, augmented reality, and other computer vision applications as you follow clear examples written in Python. Programming Computer Vision with Python explains computer vision in broad terms that won’t bog you down in theory. You get complete code samples with explanations on how to reproduce and build upon each example, along with exercises to help you apply what you’ve learned. This book is ideal for students, researchers, and enthusiasts with basic programming and standard mathematical skills. Learn techniques used in robot navigation, medical image analysis, and other computer vision applications Work with image mappings and transforms, such as texture warping and panorama creation Compute 3D reconstructions from several images of the same scene Organize images based on similarity or content, using clustering methods Build efficient image retrieval techniques to search for images based on visual content Use algorithms to classify image content and recognize objects Access the popular OpenCV library through a Python interface |
| computer programming and analysis: R Programming for Bioinformatics Robert Gentleman, 2008-07-14 Due to its data handling and modeling capabilities as well as its flexibility, R is becoming the most widely used software in bioinformatics. R Programming for Bioinformatics explores the programming skills needed to use this software tool for the solution of bioinformatics and computational biology problems.Drawing on the author's first-hand exper |
| computer programming and analysis: The Psychology of Computer Programming Gerald M. Weinberg, 1998 Discover or Revisit One of the Most Popular Books in Computing This landmark 1971 classic is reprinted with a new preface, chapter-by-chapter commentary, and straight-from-the-heart observations on topics that affect the professional life of programmers. Long regarded as one of the first books to pioneer a people-oriented approach to computing, The Psychology of Computer Programming endures as a penetrating analysis of the intelligence, skill, teamwork, and problem-solving power of the computer programmer. Finding the chapters strikingly relevant to today's issues in programming, Gerald M. Weinberg adds new insights and highlights the similarities and differences between now and then. Using a conversational style that invites the reader to join him, Weinberg reunites with some of his most insightful writings on the human side of software engineering. Topics include egoless programming, intelligence, psychological measurement, personality factors, motivation, training, social problems on large projects, problem-solving ability, programming language design, team formation, the programming environment, and much more. Dorset House Publishing is proud to make this important text available to new generations of programmers--and to encourage readers of the first edition to return to its valuable lessons. |
| computer programming and analysis: Computer Programming for Absolute Beginners Joakim Wassberg, 2020-07-31 Get to grips with the building blocks of programming languages and get started on your programming journey without a computer science degree Key FeaturesUnderstand the fundamentals of a computer program and apply the concepts you learn to different programming languagesGain the confidence to write your first computer programExplore tips, techniques, and best practices to start coding like a professional programmerBook Description Learning how to code has many advantages, and gaining the right programming skills can have a massive impact on what you can do with your current skill set and the way you advance in your career. This book will be your guide to learning computer programming easily, helping you overcome the difficulties in understanding the major constructs in any mainstream programming language. Computer Programming for Absolute Beginners starts by taking you through the building blocks of any programming language with thorough explanations and relevant examples in pseudocode. You'll understand the relationship between computer programs and programming languages and how code is executed on the computer. The book then focuses on the different types of applications that you can create with your programming knowledge. You'll delve into programming constructs, learning all about statements, operators, variables, and data types. As you advance, you'll see how to control the flow of your programs using control structures and reuse your code using functions. Finally, you'll explore best practices that will help you write code like a pro. By the end of this book, you'll be prepared to learn any programming language and take control of your career by adding coding to your skill set. What you will learnGet to grips with basic programming language concepts such as variables, loops, selection and functionsUnderstand what a program is and how the computer executes itExplore different programming languages and learn about the relationship between source code and executable codeSolve problems using various paradigms such as procedural programming, object oriented programming, and functional programmingWrite high-quality code using several coding conventions and best practicesBecome well-versed with how to track and fix bugs in your programsWho this book is for This book is for beginners who have never programmed before and are looking to enter the world of programming. This includes anyone who is about to start studying programming and wants a head start, or simply wants to learn how to program on their own. |
| computer programming and analysis: Software for Data Analysis John Chambers, 2008-06-14 John Chambers turns his attention to R, the enormously successful open-source system based on the S language. His book guides the reader through programming with R, beginning with simple interactive use and progressing by gradual stages, starting with simple functions. More advanced programming techniques can be added as needed, allowing users to grow into software contributors, benefiting their careers and the community. R packages provide a powerful mechanism for contributions to be organized and communicated. This is the only advanced programming book on R, written by the author of the S language from which R evolved. |
| computer programming and analysis: Secure Programming with Static Analysis Brian Chess, Jacob West, 2007-06-29 The First Expert Guide to Static Analysis for Software Security! Creating secure code requires more than just good intentions. Programmers need to know that their code will be safe in an almost infinite number of scenarios and configurations. Static source code analysis gives users the ability to review their work with a fine-toothed comb and uncover the kinds of errors that lead directly to security vulnerabilities. Now, there’s a complete guide to static analysis: how it works, how to integrate it into the software development processes, and how to make the most of it during security code review. Static analysis experts Brian Chess and Jacob West look at the most common types of security defects that occur today. They illustrate main points using Java and C code examples taken from real-world security incidents, showing how coding errors are exploited, how they could have been prevented, and how static analysis can rapidly uncover similar mistakes. This book is for everyone concerned with building more secure software: developers, security engineers, analysts, and testers. |
| computer programming and analysis: Types and Programming Languages Benjamin C. Pierce, 2002-01-04 A comprehensive introduction to type systems and programming languages. A type system is a syntactic method for automatically checking the absence of certain erroneous behaviors by classifying program phrases according to the kinds of values they compute. The study of type systems—and of programming languages from a type-theoretic perspective—has important applications in software engineering, language design, high-performance compilers, and security. This text provides a comprehensive introduction both to type systems in computer science and to the basic theory of programming languages. The approach is pragmatic and operational; each new concept is motivated by programming examples and the more theoretical sections are driven by the needs of implementations. Each chapter is accompanied by numerous exercises and solutions, as well as a running implementation, available via the Web. Dependencies between chapters are explicitly identified, allowing readers to choose a variety of paths through the material. The core topics include the untyped lambda-calculus, simple type systems, type reconstruction, universal and existential polymorphism, subtyping, bounded quantification, recursive types, kinds, and type operators. Extended case studies develop a variety of approaches to modeling the features of object-oriented languages. |
| computer programming and analysis: Teaching and Learning Computer Programming Richard E. Mayer, 2013-12-16 The influx of computer technology into classrooms during the past decade raises the questions -- how can we teach children to use computers productively and what effect will learning to program computers have on them? During this same period, researchers have investigated novice learning of computer programming. Teaching and Learning Computer Programming unites papers and perspectives by respected researchers of teaching and learning computer science while it summarizes and integrates major theoretical and empirical contributions. It gives a current and concise account of how instructional techniques affect student learning and how learning of programming affects students' cognitive skills. This collection is an ideal supplementary text for students and a valuable reference for professionals and researchers of education, technology and psychology, computer science, communication, developmental psychology, and industrial organization. |
| computer programming and analysis: Think Java Allen B. Downey, Chris Mayfield, 2016-05-06 Currently used at many colleges, universities, and high schools, this hands-on introduction to computer science is ideal for people with little or no programming experience. The goal of this concise book is not just to teach you Java, but to help you think like a computer scientist. You’ll learn how to program—a useful skill by itself—but you’ll also discover how to use programming as a means to an end. Authors Allen Downey and Chris Mayfield start with the most basic concepts and gradually move into topics that are more complex, such as recursion and object-oriented programming. Each brief chapter covers the material for one week of a college course and includes exercises to help you practice what you’ve learned. Learn one concept at a time: tackle complex topics in a series of small steps with examples Understand how to formulate problems, think creatively about solutions, and write programs clearly and accurately Determine which development techniques work best for you, and practice the important skill of debugging Learn relationships among input and output, decisions and loops, classes and methods, strings and arrays Work on exercises involving word games, graphics, puzzles, and playing cards |
| computer programming and analysis: Coding the Matrix Philip N. Klein, 2013-07 An engaging introduction to vectors and matrices and the algorithms that operate on them, intended for the student who knows how to program. Mathematical concepts and computational problems are motivated by applications in computer science. The reader learns by doing, writing programs to implement the mathematical concepts and using them to carry out tasks and explore the applications. Examples include: error-correcting codes, transformations in graphics, face detection, encryption and secret-sharing, integer factoring, removing perspective from an image, PageRank (Google's ranking algorithm), and cancer detection from cell features. A companion web site, codingthematrix.com provides data and support code. Most of the assignments can be auto-graded online. Over two hundred illustrations, including a selection of relevant xkcd comics. Chapters: The Function, The Field, The Vector, The Vector Space, The Matrix, The Basis, Dimension, Gaussian Elimination, The Inner Product, Special Bases, The Singular Value Decomposition, The Eigenvector, The Linear Program A new edition of this text, incorporating corrections and an expanded index, has been issued as of September 4, 2013, and will soon be available on Amazon. |
| computer programming and analysis: Programming Machine Learning Paolo Perrotta, 2020-03-31 You've decided to tackle machine learning - because you're job hunting, embarking on a new project, or just think self-driving cars are cool. But where to start? It's easy to be intimidated, even as a software developer. The good news is that it doesn't have to be that hard. Master machine learning by writing code one line at a time, from simple learning programs all the way to a true deep learning system. Tackle the hard topics by breaking them down so they're easier to understand, and build your confidence by getting your hands dirty. Peel away the obscurities of machine learning, starting from scratch and going all the way to deep learning. Machine learning can be intimidating, with its reliance on math and algorithms that most programmers don't encounter in their regular work. Take a hands-on approach, writing the Python code yourself, without any libraries to obscure what's really going on. Iterate on your design, and add layers of complexity as you go. Build an image recognition application from scratch with supervised learning. Predict the future with linear regression. Dive into gradient descent, a fundamental algorithm that drives most of machine learning. Create perceptrons to classify data. Build neural networks to tackle more complex and sophisticated data sets. Train and refine those networks with backpropagation and batching. Layer the neural networks, eliminate overfitting, and add convolution to transform your neural network into a true deep learning system. Start from the beginning and code your way to machine learning mastery. What You Need: The examples in this book are written in Python, but don't worry if you don't know this language: you'll pick up all the Python you need very quickly. Apart from that, you'll only need your computer, and your code-adept brain. |
| computer programming and analysis: Data Structures and Algorithm Analysis in Java, Third Edition Clifford A. Shaffer, 2012-09-06 Comprehensive treatment focuses on creation of efficient data structures and algorithms and selection or design of data structure best suited to specific problems. This edition uses Java as the programming language. |
| computer programming and analysis: Principles of Abstract Interpretation Patrick Cousot, 2021-09-21 Introduction to abstract interpretation, with examples of applications to the semantics, specification, verification, and static analysis of computer programs. Formal methods are mathematically rigorous techniques for the specification, development, manipulation, and verification of safe, robust, and secure software and hardware systems. Abstract interpretation is a unifying theory of formal methods that proposes a general methodology for proving the correctness of computing systems, based on their semantics. The concepts of abstract interpretation underlie such software tools as compilers, type systems, and security protocol analyzers. This book provides an introduction to the theory and practice of abstract interpretation, offering examples of applications to semantics, specification, verification, and static analysis of programming languages with emphasis on calculational design. The book covers all necessary computer science and mathematical concepts--including most of the logic, order, linear, fixpoint, and discrete mathematics frequently used in computer science--in separate chapters before they are used in the text. Each chapter offers exercises and selected solutions. Chapter topics include syntax, parsing, trace semantics, properties and their abstraction, fixpoints and their abstractions, reachability semantics, abstract domain and abstract interpreter, specification and verification, effective fixpoint approximation, relational static analysis, and symbolic static analysis. The main applications covered include program semantics, program specification and verification, program dynamic and static analysis of numerical properties and of such symbolic properties as dataflow analysis, software model checking, pointer analysis, dependency, and typing (both for forward and backward analysis), and their combinations. Principles of Abstract Interpretation is suitable for classroom use at the graduate level and as a reference for researchers and practitioners. |
| computer programming and analysis: Programming for Computations - Python Svein Linge, Hans Petter Langtangen, 2016-07-25 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. |
| computer programming and analysis: An Introduction to Python and Computer Programming Yue Zhang, 2015-07-08 This book introduces Python programming language and fundamental concepts in algorithms and computing. Its target audience includes students and engineers with little or no background in programming, who need to master a practical programming language and learn the basic thinking in computer science/programming. The main contents come from lecture notes for engineering students from all disciplines, and has received high ratings. Its materials and ordering have been adjusted repeatedly according to classroom reception. Compared to alternative textbooks in the market, this book introduces the underlying Python implementation of number, string, list, tuple, dict, function, class, instance and module objects in a consistent and easy-to-understand way, making assignment, function definition, function call, mutability and binding environments understandable inside-out. By giving the abstraction of implementation mechanisms, this book builds a solid understanding of the Python programming language. |
| computer programming and analysis: How to Design Programs, second edition Matthias Felleisen, Robert Bruce Findler, Matthew Flatt, Shriram Krishnamurthi, 2018-05-25 A completely revised edition, offering new design recipes for interactive programs and support for images as plain values, testing, event-driven programming, and even distributed programming. This introduction to programming places computer science at the core of a liberal arts education. Unlike other introductory books, it focuses on the program design process, presenting program design guidelines that show the reader how to analyze a problem statement, how to formulate concise goals, how to make up examples, how to develop an outline of the solution, how to finish the program, and how to test it. Because learning to design programs is about the study of principles and the acquisition of transferable skills, the text does not use an off-the-shelf industrial language but presents a tailor-made teaching language. For the same reason, it offers DrRacket, a programming environment for novices that supports playful, feedback-oriented learning. The environment grows with readers as they master the material in the book until it supports a full-fledged language for the whole spectrum of programming tasks. This second edition has been completely revised. While the book continues to teach a systematic approach to program design, the second edition introduces different design recipes for interactive programs with graphical interfaces and batch programs. It also enriches its design recipes for functions with numerous new hints. Finally, the teaching languages and their IDE now come with support for images as plain values, testing, event-driven programming, and even distributed programming. |
| computer programming and analysis: Thinking In Numbers Daniel Tammet, 2013-07-30 The irresistibly engaging book that enlarges one's wonder at Tammet's mind and his all-embracing vision of the world as grounded in numbers (Oliver Sacks, MD). Thinking in Numbers is the book that Daniel Tammet, mathematical savant and bestselling author, was born to write. In Tammet's world, numbers are beautiful and mathematics illuminates our lives and minds. Using anecdotes, everyday examples, and ruminations on history, literature, and more, Tammet allows us to share his unique insights and delight in the way numbers, fractions, and equations underpin all our lives. Inspired variously by the complexity of snowflakes, Anne Boleyn's eleven fingers, and his many siblings, Tammet explores questions such as why time seems to speed up as we age, whether there is such a thing as an average person, and how we can make sense of those we love. His provocative and inspiring new book will change the way you think about math and fire your imagination to view the world with fresh eyes. |
| computer programming and analysis: The R Software Pierre Lafaye de Micheaux, Rémy Drouilhet, Benoit Liquet, 2014-05-13 The contents of The R Software are presented so as to be both comprehensive and easy for the reader to use. Besides its application as a self-learning text, this book can support lectures on R at any level from beginner to advanced. This book can serve as a textbook on R for beginners as well as more advanced users, working on Windows, MacOs or Linux OSes. The first part of the book deals with the heart of the R language and its fundamental concepts, including data organization, import and export, various manipulations, documentation, plots, programming and maintenance. The last chapter in this part deals with oriented object programming as well as interfacing R with C/C++ or Fortran, and contains a section on debugging techniques. This is followed by the second part of the book, which provides detailed explanations on how to perform many standard statistical analyses, mainly in the Biostatistics field. Topics from mathematical and statistical settings that are included are matrix operations, integration, optimization, descriptive statistics, simulations, confidence intervals and hypothesis testing, simple and multiple linear regression, and analysis of variance. Each statistical chapter in the second part relies on one or more real biomedical data sets, kindly made available by the Bordeaux School of Public Health (Institut de Santé Publique, d'Épidémiologie et de Développement - ISPED) and described at the beginning of the book. Each chapter ends with an assessment section: memorandum of most important terms, followed by a section of theoretical exercises (to be done on paper), which can be used as questions for a test. Moreover, worksheets enable the reader to check his new abilities in R. Solutions to all exercises and worksheets are included in this book. |
| computer programming and analysis: C++ Programming: From Problem Analysis to Program Design D. S. Malik, 2017-05-24 Learn how to program with C++ using today’s definitive choice for your first programming language experience -- C++ PROGRAMMING: FROM PROBLEM ANALYSIS TO PROGRAM DESIGN, 8E. D.S. Malik’s time-tested, user-centered methodology incorporates a strong focus on problem-solving with full-code examples that vividly demonstrate the hows and whys of applying programming concepts and utilizing C++ to work through a problem. Thoroughly updated end-of-chapter exercises, more than 20 extensive new programming exercises, and numerous new examples drawn from Dr. Malik’s experience further strengthen the reader’s understanding of problem solving and program design in this new edition. This book highlights the most important features of C++ 14 Standard with timely discussions that ensure this edition equips you to succeed in your first programming experience and well beyond. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version. |
| computer programming and analysis: DSLs in Action Debasish Ghosh, 2010-11-30 Your success—and sanity—are closer at hand when you work at a higher level of abstraction, allowing your attention to be on the business problem rather than the details of the programming platform. Domain Specific Languages—little languages implemented on top of conventional programming languages—give you a way to do this because they model the domain of your business problem. DSLs in Action introduces the concepts and definitions a developer needs to build high-quality domain specific languages. It provides a solid foundation to the usage as well as implementation aspects of a DSL, focusing on the necessity of applications speaking the language of the domain. After reading this book, a programmer will be able to design APIs that make better domain models. For experienced developers, the book addresses the intricacies of domain language design without the pain of writing parsers by hand. The book discusses DSL usage and implementations in the real world based on a suite of JVM languages like Java, Ruby, Scala, and Groovy. It contains code snippets that implement real world DSL designs and discusses the pros and cons of each implementation. Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book. What's Inside Tested, real-world examples How to find the right level of abstraction Using language features to build internal DSLs Designing parser/combinator-based little languages |
| computer programming and analysis: Deep Learning for Coders with fastai and PyTorch Jeremy Howard, Sylvain Gugger, 2020-06-29 Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala |
| computer programming and analysis: Computer Programming with COMIT II Victor H. Yngve, 1972 COMIT is a symbol-manipulating (or string-processing) language designed to handle texts, words, characters, logical expressions, descriptors, attributes, tags, and the like, and to manipulate them in the computer in ways that are relevant to problems in a variety of fields. COMIT is a general-purpose language which has been most efficiently used for problems in linguistics, mechanical translation of languages, information retrieval, modeling of cognitive processes, theorem proving, game playing, content analysis, graph theory, and many other primarily nonnumerical problems. In addition, COMIT serves as an introduction to a whole class of programming languages and language design features. This book is derived from two older manuals that have been out of print for some time, An Introduction to COMIT Programmingand COMIT Programmers' Reference Manual(MIT Press 1962). The programs originally run under COMIT will still run under COMIT II; yet this new publication includes improvements in the language that allow easier programming plus additional facilities. COMIT II is designed to be easily learned and used both as a language for a first course in programming for students in a wide range of disciplines and as a second or third programming language for more advanced students who can use this book for self-study. Included are numerous exercises and problems along with answers, as well as problems to be run on the computer under a problem-grader program that may be obtained from the author. COMIT II is fully available on the IBM 7000 series of computers, including the 709, 7090, 7040, and 7044, and a more recent implementation is available for the IBM 360. |
| computer programming and analysis: R for Data Science Hadley Wickham, Garrett Grolemund, 2016-12-12 Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true signals in your dataset Communicate—learn R Markdown for integrating prose, code, and results |
| computer programming and analysis: NBS Special Publication , 1965 |
| computer programming and analysis: The Art of Differentiating Computer Programs Uwe Naumann, 2012-01-01 This is the first entry-level book on algorithmic (also known as automatic) differentiation (AD), providing fundamental rules for the generation of first- and higher-order tangent-linear and adjoint code. The author covers the mathematical underpinnings as well as how to apply these observations to real-world numerical simulation programs. Readers will find: examples and exercises, including hints to solutions; the prototype AD tools dco and dcc for use with the examples and exercises; first- and higher-order tangent-linear and adjoint modes for a limited subset of C/C++, provided by the derivative code compiler dcc; a supplementary website containing sources of all software discussed in the book, additional exercises and comments on their solutions (growing over the coming years), links to other sites on AD, and errata. |
| computer programming and analysis: The Art of Computer Programming Donald Ervin Knuth, 1997 Donald Knuth is Professor Emeritus of the Art of Computer Programming at Stanford University, and is well-known worldwide as the creator of the Tex typesetting language. Here he presents the third volume of his guide to computer programming. |
| computer programming and analysis: A Practical Introduction to Data Structures and Algorithm Analysis Clifford A. Shaffer, 2001 This practical text contains fairly traditional coverage of data structures with a clear and complete use of algorithm analysis, and some emphasis on file processing techniques as relevant to modern programmers. It fully integrates OO programming with these topics, as part of the detailed presentation of OO programming itself.Chapter topics include lists, stacks, and queues; binary and general trees; graphs; file processing and external sorting; searching; indexing; and limits to computation.For programmers who need a good reference on data structures. |
| computer programming and analysis: Program Analysis--a Problem in Man-computer Communication Joseph Harris Green, 1970 This report presents a display oriented scheme to help the higher level language computer programmer to debug and analyze programs. Timed visual interpretive execution and a variety of user instituted functions permit an informative dialogue between the man and the computer. A prototype implementation of the system using FORTRAN as the higher level language is described, and the results are discussed from the point of view of the system developer as well as the point of view of the user. Also treated is the problem of what a time-sharing system should do to make graphical input tablets useful devices in a time-shared environment. |
| computer programming and analysis: USAF Formal Schools United States. Department of the Air Force, 1987 |
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