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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.
Подробнее
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.
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: 3