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1.
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H 32
Harwani, B. M.,
Practical C programming : : solutions for modern C developers to create efficient and well-structured programs / / B.M. Harwani. - Birmingham, UK : : Packt Publishing,, 2020. - 1 online resource (1 volume) : : il. - Includes bibliographical references. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/89F3D57B-DBD5-4B12-96DD-3EBCFE24D704. - ISBN 9781838647988. - ISBN 1838647988
Description based on online resource; title from title page (Safari, viewed July 28, 2020).
Параллельные издания: Print version: : Harwani, B.M. Practical C Programming : Solutions for Modern C Developers to Create Efficient and Well-Structured Programs. - Birmingham : Packt Publishing, Limited, ©2020
~РУБ DDC 005.13/3
Рубрики: C (Computer program language)
Computer programming.
C (Computer program language)
Computer programming
Аннотация: Practical C Programming will teach you how to deal with C and its idiosyncrasies, and benefit from its new features, through bite-sized recipes. Each recipe in the book addresses a specific problem through a discussion that reveals and explains the solution to the recipe. This book will teach all you need to know to become a better C programmer.
H 32
Harwani, B. M.,
Practical C programming : : solutions for modern C developers to create efficient and well-structured programs / / B.M. Harwani. - Birmingham, UK : : Packt Publishing,, 2020. - 1 online resource (1 volume) : : il. - Includes bibliographical references. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/89F3D57B-DBD5-4B12-96DD-3EBCFE24D704. - ISBN 9781838647988. - ISBN 1838647988
Description based on online resource; title from title page (Safari, viewed July 28, 2020).
Параллельные издания: Print version: : Harwani, B.M. Practical C Programming : Solutions for Modern C Developers to Create Efficient and Well-Structured Programs. - Birmingham : Packt Publishing, Limited, ©2020
Рубрики: C (Computer program language)
Computer programming.
C (Computer program language)
Computer programming
Аннотация: Practical C Programming will teach you how to deal with C and its idiosyncrasies, and benefit from its new features, through bite-sized recipes. Each recipe in the book addresses a specific problem through a discussion that reveals and explains the solution to the recipe. This book will teach all you need to know to become a better C programmer.
2.
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S 19
Samoylov, Nick.
LEARNING RXJAVA : : BUILD CONCURRENT APPLICATIONS USING REACTIVE PROGRAMMING WITH THE LATEST FEATURES OF RXJAVA 3. / Nick. Samoylov, Nield, Thomas. - [Place of publication not identified] : PACKT Publishing,, 2020. - 1 online resource. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/18F1110A-3A99-44CD-B605-F0B8FDA68074. - ISBN 1789952212 (electronic bk.). - ISBN 9781789952216 (electronic bk.)
Online resource; title from PDF title page (EBSCO, viewed June 18, 2020).
~РУБ DDC 005.133
Рубрики: Application software--Development.
Java (Computer program language)
Computer programming.
Доп.точки доступа:
Nield, Thomas.
S 19
Samoylov, Nick.
LEARNING RXJAVA : : BUILD CONCURRENT APPLICATIONS USING REACTIVE PROGRAMMING WITH THE LATEST FEATURES OF RXJAVA 3. / Nick. Samoylov, Nield, Thomas. - [Place of publication not identified] : PACKT Publishing,, 2020. - 1 online resource. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/18F1110A-3A99-44CD-B605-F0B8FDA68074. - ISBN 1789952212 (electronic bk.). - ISBN 9781789952216 (electronic bk.)
Online resource; title from PDF title page (EBSCO, viewed June 18, 2020).
Рубрики: Application software--Development.
Java (Computer program language)
Computer programming.
Доп.точки доступа:
Nield, Thomas.
3.
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B 87
Brock, Kevin,.
Rhetorical code studies : : discovering arguments in and around code / / Kevin Brock. - 3998/mpub. - Ann Arbor : : University of Michigan Press,, ©2019. - 1 online resource (ix, 213 pages) : il ( час. мин.), 3998/mpub. - (Sweetland digital rhetoric collaborative). - Includes bibliographical references and index. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/FDB1DB0C-B85E-468F-9705-6A069EC588C6. - ISBN 9780472125005 (electronic book). - ISBN 0472901044 (electronic book). - ISBN 9780472901043 (Open Access). - ISBN 0472125001 (Open Access). - ISBN 0472131273. - ISBN 9780472131273
Online resource; title from digital title page (viewed on April 01, 2019).
Параллельные издания: Print version: : Brock, Kevin. Rhetorical code studies. - Ann Arbor : University of Michigan Press, [2019]. - ISBN 9780472131273
Содержание:
Intro; Contents; List of Tables; List of Practice Scripts; List of Figures; Introduction; 1. Toward the Rhetorical Study of Code; What Does Rhetorical Code Studies Involve?; Digital Rhetoric; Critical Code Studies; Software Studies; Technical Communication; Rhetorical Code Studies' Gains and Contributions; 2. Rhetoric and the Algorithm; From Algorithm to Algorithmic Culture; Algorithmic Criticism in the Humanities; Arguments in Code as Algorithmic Meaning Making; Conclusions; 3. "I Have No Damn Idea Why This Is So Convoluted": Analyzing Arguments Surrounding Code
Rhetorical Scholarship on Online Discourse Communities; The Rhetorical and Social Makeup of Open Source Software Development Communities; Developers' Rhetorical Awareness of Their Coding Practices; Conclusions; 4. Developing Arguments in Code: The Case of Mozilla Firefox; Mozilla Firefox: A Code Study; Conclusions; 5. Composing in Code: A Brief Engagement with JavaScript; Procedural Progymnasmata; Exercises in Repetition: Looping; Exercises in Style: FizzBuzz; Exercises in Repetition: Object Creation; Exercises in Arrangement: Bubble Sort; Exercises in Invention: enthymemeGenerator.js
Conclusions; 6. Conclusions; Rhetorical Code Studies Thus Far; Assessing Computational Action; A Future for Rhetorical Code Studies; Bibliography; Index
~РУБ DDC 005.13
Рубрики: Coding theory.
Rhetoric--Data processing.
Software engineering--Psychological aspects.
Computer algorithms--Psychological aspects.
Online social networks.
Computer programming--software development.
Information technology: general issues.
Programming & scripting languages: general.
COMPUTERS--Software Development & Engineering--General.
COMPUTERS--General.
Coding theory.
Online social networks.
Аннотация: "In Rhetorical Code Studies, Kevin Brock explores how software code serves as a means of meaningful communication through which amateur and professional software developers construct arguments--arguments that are not only made up of logical procedures but also of implicit and explicit claims about how a given program works (or should work). These claims appear as procedures and as conventional discourse in the form of code comments and in email messages, forum posts, and other venues for conversation with other developers. To investigate the rhetorical qualities of code, Brock extends ongoing conversations in rhetoric and composition on software by turning to a number of case examples ranging from large, well-known projects like Mozilla Firefox to small-scale programs like the "FizzBuzz" test common in many programming job interviews. These examples, which involve specific examination of code texts as well as the contexts surrounding their composition, demonstrate the variety and depth of rhetorical activity taking place in and around code, from individual differences in style to changes in large-scale community norms"--
B 87
Brock, Kevin,.
Rhetorical code studies : : discovering arguments in and around code / / Kevin Brock. - 3998/mpub. - Ann Arbor : : University of Michigan Press,, ©2019. - 1 online resource (ix, 213 pages) : il ( час. мин.), 3998/mpub. - (Sweetland digital rhetoric collaborative). - Includes bibliographical references and index. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/FDB1DB0C-B85E-468F-9705-6A069EC588C6. - ISBN 9780472125005 (electronic book). - ISBN 0472901044 (electronic book). - ISBN 9780472901043 (Open Access). - ISBN 0472125001 (Open Access). - ISBN 0472131273. - ISBN 9780472131273
Online resource; title from digital title page (viewed on April 01, 2019).
Параллельные издания: Print version: : Brock, Kevin. Rhetorical code studies. - Ann Arbor : University of Michigan Press, [2019]. - ISBN 9780472131273
Содержание:
Intro; Contents; List of Tables; List of Practice Scripts; List of Figures; Introduction; 1. Toward the Rhetorical Study of Code; What Does Rhetorical Code Studies Involve?; Digital Rhetoric; Critical Code Studies; Software Studies; Technical Communication; Rhetorical Code Studies' Gains and Contributions; 2. Rhetoric and the Algorithm; From Algorithm to Algorithmic Culture; Algorithmic Criticism in the Humanities; Arguments in Code as Algorithmic Meaning Making; Conclusions; 3. "I Have No Damn Idea Why This Is So Convoluted": Analyzing Arguments Surrounding Code
Rhetorical Scholarship on Online Discourse Communities; The Rhetorical and Social Makeup of Open Source Software Development Communities; Developers' Rhetorical Awareness of Their Coding Practices; Conclusions; 4. Developing Arguments in Code: The Case of Mozilla Firefox; Mozilla Firefox: A Code Study; Conclusions; 5. Composing in Code: A Brief Engagement with JavaScript; Procedural Progymnasmata; Exercises in Repetition: Looping; Exercises in Style: FizzBuzz; Exercises in Repetition: Object Creation; Exercises in Arrangement: Bubble Sort; Exercises in Invention: enthymemeGenerator.js
Conclusions; 6. Conclusions; Rhetorical Code Studies Thus Far; Assessing Computational Action; A Future for Rhetorical Code Studies; Bibliography; Index
Рубрики: Coding theory.
Rhetoric--Data processing.
Software engineering--Psychological aspects.
Computer algorithms--Psychological aspects.
Online social networks.
Computer programming--software development.
Information technology: general issues.
Programming & scripting languages: general.
COMPUTERS--Software Development & Engineering--General.
COMPUTERS--General.
Coding theory.
Online social networks.
Аннотация: "In Rhetorical Code Studies, Kevin Brock explores how software code serves as a means of meaningful communication through which amateur and professional software developers construct arguments--arguments that are not only made up of logical procedures but also of implicit and explicit claims about how a given program works (or should work). These claims appear as procedures and as conventional discourse in the form of code comments and in email messages, forum posts, and other venues for conversation with other developers. To investigate the rhetorical qualities of code, Brock extends ongoing conversations in rhetoric and composition on software by turning to a number of case examples ranging from large, well-known projects like Mozilla Firefox to small-scale programs like the "FizzBuzz" test common in many programming job interviews. These examples, which involve specific examination of code texts as well as the contexts surrounding their composition, demonstrate the variety and depth of rhetorical activity taking place in and around code, from individual differences in style to changes in large-scale community norms"--
4.
Подробнее
C 58
Ciaburro, Giuseppe,.
Hands-on simulation modeling with Python : : develop simulation models to get accurate results and enhance decision-making processes / / Giuseppe Ciaburro. - Birmingham, UK : : Packt Publishing,, 2020. - 1 online resource (1 volume) : : il. - Includes bibliographical references. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/9C4C9EA4-37AB-4EFC-8A6B-362ED4B308DE. - ISBN 9781838988654. - ISBN 1838988653
Description based on online resource; title from cover (Safari, viewed October 27, 2020).
Параллельные издания: Print version: : Ciaburro, Giuseppe Hands-On Simulation Modeling with Python : Develop Simulation Models to Get Accurate Results and Enhance Decision-Making Processes. - Birmingham : Packt Publishing, Limited,c2020
Содержание:
Cover -- Title Page -- Copyright and Credits -- About Packt -- Contributors -- Table of Contents -- Preface -- Section 1: Getting Started with Numerical Simulation -- Chapter 1: Introducing Simulation Models -- Introducing simulation models -- Decision-making workflow -- Comparing modeling and simulation -- Pros and cons of simulation modeling -- Simulation modeling terminology -- Classifying simulation models -- Comparing static and dynamic models -- Comparing deterministic and stochastic models -- Comparing continuous and discrete models -- Approaching a simulation-based problem
Problem analysis -- Data collection -- Setting up the simulation model -- Simulation software selection -- Verification of the software solution -- Validation of the simulation model -- Simulation and analysis of results -- Dynamical systems modeling -- Managing workshop machinery -- Simple harmonic oscillator -- Predator-prey model -- Summary -- Chapter 2: Understanding Randomness and Random Numbers -- Technical requirements -- Stochastic processes -- Types of stochastic process -- Examples of stochastic processes -- The Bernoulli process -- Random walk -- The Poisson process
Random number simulation -- Probability distribution -- Properties of random numbers -- The pseudorandom number generator -- The pros and cons of a random number generator -- Random number generation algorithms -- Linear congruential generator -- Random numbers with uniform distribution -- Lagged Fibonacci generator -- Testing uniform distribution -- The chi-squared test -- Uniformity test -- Exploring generic methods for random distributions -- The inverse transform sampling method -- The acceptance-rejection method -- Random number generation using Python -- Introducing the random module
The random.random() function -- The random.seed() function -- The random.uniform() function -- The random.randint() function -- The random.choice() function -- The random.sample() function -- Generating real-valued distributions -- Summary -- Chapter 3: Probability and Data Generation Processes -- Technical requirements -- Explaining probability concepts -- Types of events -- Calculating probability -- Probability definition with an example -- Understanding Bayes' theorem -- Compound probability -- Bayes' theorem -- Exploring probability distributions -- Probability density function
Mean and variance -- Uniform distribution -- Binomial distribution -- Normal distribution -- Summary -- Section 2: Simulation Modeling Algorithms and Techniques -- Chapter 4: Exploring Monte Carlo Simulations -- Technical requirements -- Introducing Monte Carlo simulation -- Monte Carlo components -- First Monte Carlo application -- Monte Carlo applications -- Applying the Monte Carlo method for Pi estimation -- Understanding the central limit theorem -- Law of large numbers -- Central limit theorem -- Applying Monte Carlo simulation -- Generating probability distributions
~РУБ DDC 003.3
Рубрики: Python (Computer program language)
Computer simulation.
Simulation methods.
Decision making--Data processing.
Computer programming.
Computer simulation.
Python (Computer program language)
Аннотация: Developers working with the simulation models will be able to put their knowledge to work with this practical guide. You will work with real-world data to uncover various patterns used in complex systems using Python. The book provides a hands-on approach to implementation and associated methodologies to improve or optimize systems.
C 58
Ciaburro, Giuseppe,.
Hands-on simulation modeling with Python : : develop simulation models to get accurate results and enhance decision-making processes / / Giuseppe Ciaburro. - Birmingham, UK : : Packt Publishing,, 2020. - 1 online resource (1 volume) : : il. - Includes bibliographical references. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/9C4C9EA4-37AB-4EFC-8A6B-362ED4B308DE. - ISBN 9781838988654. - ISBN 1838988653
Description based on online resource; title from cover (Safari, viewed October 27, 2020).
Параллельные издания: Print version: : Ciaburro, Giuseppe Hands-On Simulation Modeling with Python : Develop Simulation Models to Get Accurate Results and Enhance Decision-Making Processes. - Birmingham : Packt Publishing, Limited,c2020
Содержание:
Cover -- Title Page -- Copyright and Credits -- About Packt -- Contributors -- Table of Contents -- Preface -- Section 1: Getting Started with Numerical Simulation -- Chapter 1: Introducing Simulation Models -- Introducing simulation models -- Decision-making workflow -- Comparing modeling and simulation -- Pros and cons of simulation modeling -- Simulation modeling terminology -- Classifying simulation models -- Comparing static and dynamic models -- Comparing deterministic and stochastic models -- Comparing continuous and discrete models -- Approaching a simulation-based problem
Problem analysis -- Data collection -- Setting up the simulation model -- Simulation software selection -- Verification of the software solution -- Validation of the simulation model -- Simulation and analysis of results -- Dynamical systems modeling -- Managing workshop machinery -- Simple harmonic oscillator -- Predator-prey model -- Summary -- Chapter 2: Understanding Randomness and Random Numbers -- Technical requirements -- Stochastic processes -- Types of stochastic process -- Examples of stochastic processes -- The Bernoulli process -- Random walk -- The Poisson process
Random number simulation -- Probability distribution -- Properties of random numbers -- The pseudorandom number generator -- The pros and cons of a random number generator -- Random number generation algorithms -- Linear congruential generator -- Random numbers with uniform distribution -- Lagged Fibonacci generator -- Testing uniform distribution -- The chi-squared test -- Uniformity test -- Exploring generic methods for random distributions -- The inverse transform sampling method -- The acceptance-rejection method -- Random number generation using Python -- Introducing the random module
The random.random() function -- The random.seed() function -- The random.uniform() function -- The random.randint() function -- The random.choice() function -- The random.sample() function -- Generating real-valued distributions -- Summary -- Chapter 3: Probability and Data Generation Processes -- Technical requirements -- Explaining probability concepts -- Types of events -- Calculating probability -- Probability definition with an example -- Understanding Bayes' theorem -- Compound probability -- Bayes' theorem -- Exploring probability distributions -- Probability density function
Mean and variance -- Uniform distribution -- Binomial distribution -- Normal distribution -- Summary -- Section 2: Simulation Modeling Algorithms and Techniques -- Chapter 4: Exploring Monte Carlo Simulations -- Technical requirements -- Introducing Monte Carlo simulation -- Monte Carlo components -- First Monte Carlo application -- Monte Carlo applications -- Applying the Monte Carlo method for Pi estimation -- Understanding the central limit theorem -- Law of large numbers -- Central limit theorem -- Applying Monte Carlo simulation -- Generating probability distributions
Рубрики: Python (Computer program language)
Computer simulation.
Simulation methods.
Decision making--Data processing.
Computer programming.
Computer simulation.
Python (Computer program language)
Аннотация: Developers working with the simulation models will be able to put their knowledge to work with this practical guide. You will work with real-world data to uncover various patterns used in complex systems using Python. The book provides a hands-on approach to implementation and associated methodologies to improve or optimize systems.
Page 1, Results: 4