Introduction To Computational Models With Python
DOWNLOAD
Download Introduction To Computational Models With Python PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Introduction To Computational Models With Python book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Introduction To Computational Models With Python
DOWNLOAD
Author : Jose M. Garrido
language : en
Publisher: CRC Press
Release Date : 2015-08-28
Introduction To Computational Models With Python written by Jose M. Garrido and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-08-28 with Computers categories.
Introduction to Computational Models with Python explains how to implement computational models using the flexible and easy-to-use Python programming language. The book uses the Python programming language interpreter and several packages from the huge Python Library that improve the performance of numerical computing, such as the Numpy and Scipy m
Introduction To Computation And Programming Using Python Third Edition
DOWNLOAD
Author : John V. Guttag
language : en
Publisher: MIT Press
Release Date : 2021-01-26
Introduction To Computation And Programming Using Python Third Edition written by John V. Guttag and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-26 with Computers categories.
The new edition of an introduction to the art of computational problem solving using Python. This book introduces students with little or no prior programming experience to the art of computational problem solving using Python and various Python libraries, including numpy, matplotlib, random, pandas, and sklearn. It provides students with skills that will enable them to make productive use of computational techniques, including some of the tools and techniques of data science for using computation to model and interpret data as well as substantial material on machine learning. All of the code in the book and an errata sheet are available on the book’s web page on the MIT Press website.
Computational Modeling And Visualization Of Physical Systems With Python
DOWNLOAD
Author : Jay Wang
language : en
Publisher: John Wiley & Sons
Release Date : 2015-12-21
Computational Modeling And Visualization Of Physical Systems With Python written by Jay Wang and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-12-21 with Science categories.
Computational Modeling, by Jay Wang introduces computational modeling and visualization of physical systems that are commonly found in physics and related areas. The authors begin with a framework that integrates model building, algorithm development, and data visualization for problem solving via scientific computing. Through carefully selected problems, methods, and projects, the reader is guided to learning and discovery by actively doing rather than just knowing physics.
Introduction To Modeling And Simulation With Matlab And Python
DOWNLOAD
Author : Steven I. Gordon
language : en
Publisher: CRC Press
Release Date : 2017-07-12
Introduction To Modeling And Simulation With Matlab And Python written by Steven I. Gordon and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-12 with Computers categories.
Introduction to Modeling and Simulation with MATLAB and Python is intended for students and professionals in science, social science, and engineering that wish to learn the principles of computer modeling, as well as basic programming skills. The book content focuses on meeting a set of basic modeling and simulation competencies that were developed as part of several National Science Foundation grants. Even though computer science students are much more expert programmers, they are not often given the opportunity to see how those skills are being applied to solve complex science and engineering problems and may also not be aware of the libraries used by scientists to create those models. The book interleaves chapters on modeling concepts and related exercises with programming concepts and exercises. The authors start with an introduction to modeling and its importance to current practices in the sciences and engineering. They introduce each of the programming environments and the syntax used to represent variables and compute mathematical equations and functions. As students gain more programming expertise, the authors return to modeling concepts, providing starting code for a variety of exercises where students add additional code to solve the problem and provide an analysis of the outcomes. In this way, the book builds both modeling and programming expertise with a "just-in-time" approach so that by the end of the book, students can take on relatively simple modeling example on their own. Each chapter is supplemented with references to additional reading, tutorials, and exercises that guide students to additional help and allows them to practice both their programming and analytical modeling skills. In addition, each of the programming related chapters is divided into two parts – one for MATLAB and one for Python. In these chapters, the authors also refer to additional online tutorials that students can use if they are having difficulty with any of the topics. The book culminates with a set of final project exercise suggestions that incorporate both the modeling and programming skills provided in the rest of the volume. Those projects could be undertaken by individuals or small groups of students. The companion website at http://www.intromodeling.com provides updates to instructions when there are substantial changes in software versions, as well as electronic copies of exercises and the related code. The website also offers a space where people can suggest additional projects they are willing to share as well as comments on the existing projects and exercises throughout the book. Solutions and lecture notes will also be available for qualifying instructors.
A Student S Guide To Python For Physical Modeling
DOWNLOAD
Author : Jesse M. Kinder
language : en
Publisher: Princeton University Press
Release Date : 2021-08-03
A Student S Guide To Python For Physical Modeling written by Jesse M. Kinder and has been published by Princeton University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-03 with Science categories.
A fully updated tutorial on the basics of the Python programming language for science students Python is a computer programming language that has gained popularity throughout the sciences. This fully updated second edition of A Student's Guide to Python for Physical Modeling aims to help you, the student, teach yourself enough of the Python programming language to get started with physical modeling. You will learn how to install an open-source Python programming environment and use it to accomplish many common scientific computing tasks: importing, exporting, and visualizing data; numerical analysis; and simulation. No prior programming experience is assumed. This guide introduces a wide range of useful tools, including: Basic Python programming and scripting Numerical arrays Two- and three-dimensional graphics Animation Monte Carlo simulations Numerical methods, including solving ordinary differential equations Image processing Numerous code samples and exercises—with solutions—illustrate new ideas as they are introduced. This guide also includes supplemental online resources: code samples, data sets, tutorials, and more. This edition includes new material on symbolic calculations with SymPy, an introduction to Python libraries for data science and machine learning (pandas and sklearn), and a primer on Python classes and object-oriented programming. A new appendix also introduces command line tools and version control with Git.
Computational Modeling By Case Study
DOWNLOAD
Author : Zachary del Rosario
language : en
Publisher: Cambridge Scholars Publishing
Release Date : 2024-04-02
Computational Modeling By Case Study written by Zachary del Rosario and has been published by Cambridge Scholars Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-02 with Mathematics categories.
Mathematical models power the modern world; they allow us to design safe buildings, investigate changes to the climate, and study the transmission of diseases through a population. However, all models are uncertain: building contractors deviate from the planned design, humans impact the climate unpredictably, and diseases mutate and change. Modern advances in mathematics and statistics provide us with techniques to understand and quantify these sources of uncertainty, allowing us to predict and design with confidence. This book presents a comprehensive treatment of uncertainty: its conceptual nature, techniques to quantify uncertainty, and numerous examples to illustrate sound approaches. Several case studies are discussed in detail to demonstrate an end-to-end treatment of scientific modeling under uncertainty, including framing the problem, building and assessing a model, and answering meaningful questions. The book illustrates a computational approach with the Python package Grama, presenting fully reproducible examples that students and practitioners can quickly adapt to their own problems.
Principles Of Soft Computing Using Python Programming
DOWNLOAD
Author : Gypsy Nandi
language : en
Publisher: John Wiley & Sons
Release Date : 2023-12-19
Principles Of Soft Computing Using Python Programming written by Gypsy Nandi and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-19 with Computers categories.
Principles of Soft Computing Using Python Programming An accessible guide to the revolutionary techniques of soft computing Soft computing is a computing approach designed to replicate the human mind’s unique capacity to integrate uncertainty and imprecision into its reasoning. It is uniquely suited to computing operations where rigid analytical models will fail to account for the variety and ambiguity of possible solutions. As machine learning and artificial intelligence become more and more prominent in the computing landscape, the potential for soft computing techniques to revolutionize computing has never been greater. Principles of Soft Computing Using Python Programming provides readers with the knowledge required to apply soft computing models and techniques to real computational problems. Beginning with a foundational discussion of soft or fuzzy computing and its differences from hard computing, it describes different models for soft computing and their many applications, both demonstrated and theoretical. The result is a set of tools with the potential to produce new solutions to the thorniest computing problems. Readers of Principles of Soft Computing Using Python Programming will also find: Each chapter accompanied with Python codes and step-by-step comments to illustrate applications Detailed discussion of topics including artificial neural networks, rough set theory, genetic algorithms, and more Exercises at the end of each chapter including both short- and long-answer questions to reinforce learning Principles of Soft Computing Using Python Programming is ideal for researchers and engineers in a variety of fields looking for new solutions to computing problems, as well as for advanced students in programming or the computer sciences.
Computational Neuroscience And Cognitive Modelling
DOWNLOAD
Author : Britt Anderson
language : en
Publisher: SAGE
Release Date : 2014-01-08
Computational Neuroscience And Cognitive Modelling written by Britt Anderson and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-01-08 with Psychology categories.
"For the neuroscientist or psychologist who cringes at the sight of mathematical formulae and whose eyes glaze over at terms like differential equations, linear algebra, vectors, matrices, Bayes’ rule, and Boolean logic, this book just might be the therapy needed." - Anjan Chatterjee, Professor of Neurology, University of Pennsylvania "Anderson provides a gentle introduction to computational aspects of psychological science, managing to respect the reader’s intelligence while also being completely unintimidating. Using carefully-selected computational demonstrations, he guides students through a wide array of important approaches and tools, with little in the way of prerequisites...I recommend it with enthusiasm." - Asohan Amarasingham, The City University of New York This unique, self-contained and accessible textbook provides an introduction to computational modelling neuroscience accessible to readers with little or no background in computing or mathematics. Organized into thematic sections, the book spans from modelling integrate and firing neurons to playing the game Rock, Paper, Scissors in ACT-R. This non-technical guide shows how basic knowledge and modern computers can be combined for interesting simulations, progressing from early exercises utilizing spreadsheets, to simple programs in Python. Key Features include: Interleaved chapters that show how traditional computing constructs are simply disguised versions of the spread sheet methods. Mathematical facts and notation needed to understand the modelling methods are presented at their most basic and are interleaved with biographical and historical notes for contex. Numerous worked examples to demonstrate the themes and procedures of cognitive modelling. An excellent text for postgraduate students taking courses in research methods, computational neuroscience, computational modelling, cognitive science and neuroscience. It will be especially valuable to psychology students.
Logic Language Information And Computation
DOWNLOAD
Author : Jouko Väänänen
language : en
Publisher: Springer
Release Date : 2016-08-05
Logic Language Information And Computation written by Jouko Väänänen and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-08-05 with Mathematics categories.
Edited in collaboration with FoLLI, the Association of Logic, Language and Information this book constitutes the refereed proceedings of the 23rd Workshop on Logic, Language, Information and Communication, WoLLIC 2016, held in Puebla, Mexico, in August 2016.The 23 contributed papers, presented together with 9 invited lectures and tutorials, were carefully reviewed and selected from 33 submissions. The focus of the workshop is to provide a forum on inter-disciplinary research involving formal logic, computing and programming theory, and natural language and reasoning.
Proceedings Of 2nd International Conference On Mathematical Modeling And Computational Science
DOWNLOAD
Author : Sheng-Lung Peng
language : en
Publisher: Springer Nature
Release Date : 2022-06-28
Proceedings Of 2nd International Conference On Mathematical Modeling And Computational Science written by Sheng-Lung Peng and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-28 with Technology & Engineering categories.
The conference proceeding of ICMMCS 2021 presents most recent scientific and technological advances in the fields of engineering mathematics and computational science to strengthen the links in the scientific community. It is a collection of high-quality, peer-reviewed research papers presented at the Second International Conference on Mathematical Modeling and Computational Science (ICMMCS 2021), held online during October 29–30, 2021. The topics covered in the book are mathematical logic and foundations, numerical analysis, neural networks, fuzzy set theory, coding theory, higher algebra, number theory, graph theory and combinatory, computation in complex networks, calculus, differential educations and integration, application of soft computing, knowledge engineering, machine learning, artificial intelligence, big data and data analytics, high-performance computing, network and device security, Internet of Things (IoT).