gigant introduction to computation and programming using python cinenwobednddqactawr
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Introduction to Computation and Programming Using Python, third edition MIT Press Ltd
Książki / Literatura obcojęzyczna
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.
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Introduction To Computation And Programming Using Python
Książki Obcojęzyczne>Angielskie>Computing & information technology>Computer programming / software development>Programming & scripting lan...
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Microsoft Python Certification Exam 98-281 & PCEP - Preparation Guide: Introduction To Programming Using Python, PCEP - Certified Entry Level Python P Independently Published
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Sklep: Libristo.pl
Django: Application Development Using Python: An Introduction to Django INDEPENDENTLY PUBLISHED
Książki / Literatura obcojęzyczna
The Python practical guide is targeted for both learner and educators who want to implement the python programming with minimum coding skill.This book also covers the basics of Django python framework.In this book you will find the examples of each concept
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Introduction to Python for Engineers and Scientists APress
Książki / Literatura obcojęzyczna
Familiarize yourself with the basics of Python for engineering and scientific computations using this concise, practical tutorial that is focused on writing code to learn concepts. Introduction to Python is useful for industry engineers, researchers, and students who are looking for open-source solutions for numerical computation.In this book you will learn by doing, avoiding technical jargon, which makes the concepts easy to learn. First you'll see how to run basic calculations, absorbing technical complexities incrementally as you progress toward advanced topics. Throughout, the language is kept simple to ensure that readers at all levels can grasp the concepts.What You'll LearnUnderstand the fundamentals of the Python programming languageApply Python to numerical computational programming projects in engineering and scienceDiscover the Pythonic way of lifeApply data types, operators, and arrays Carry out plotting for visualizationWork with functions and loops Who This Book Is ForEngineers, scientists, researchers, and students who are new to Python. Some prior programming experience would be helpful but not required.
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Primer on Scientific Programming with Python Springer-Verlag Berlin and Heidelberg GmbH & Co....
Książki / Literatura obcojęzyczna
The book serves as a first introduction to computer programming of scientific applications, using the high-level Python language. The exposition is example and problem-oriented, where the applications are taken from mathematics, numerical calculus, statistics, physics, biology and finance. The book teaches "Matlab-style" and procedural programming as well as object-oriented programming. High school mathematics is a required background and it is advantageous to study classical and numerical one-variable calculus in parallel with reading this book. Besides learning how to program computers, the reader will also learn how to solve mathematical problems, arising in various branches of science and engineering, with the aid of numerical methods and programming. By blending programming, mathematics and scientific applications, the book lays a solid foundation for practicing computational science.§§From the reviews: Langtangen ... does an excellent job of introducing programming as a set of skills in problem solving. He guides the reader into thinking properly about producing program logic and data structures for modeling real-world problems using objects and functions and embracing the object-oriented paradigm. ... Summing Up: Highly recommended. §§F. H. Wild III, Choice, Vol. 47 (8), April 2010§§Those of us who have learned scientific programming in Python 'on the streets' could be a little jealous of students who have the opportunity to take a course out of Langtangen's Primer."§ John D. Cook, The Mathematical Association of America, September 2011§§This book goes through Python in particular, and programming in general, via tasks that scientists will likely perform. It contains valuable information for students new to scientific computing and would be the perfect bridge between an introduction to programming and an advanced course on numerical methods or computational science.§ Alex Small, IEEE, CiSE Vol. 14 (2), March _ /April 2012 §§"This fourth edition is a§wonderful, inclusive textbook that covers pretty much everything one needs to§know to go from zero to fairly sophisticated scientific programming in Python..."§Joan Horvath, Computing Reviews, March§2015 §§
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Dynamical Systems with Applications using Python Springer Nature Switzerland AG
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This textbook provides a broad introduction to continuous and discrete dynamical systems. With its hands-on approach, the text leads the reader from basic theory to recently published research material in nonlinear ordinary differential equations, nonlinear optics, multifractals, neural networks, and binary oscillator computing. Dynamical Systems with Applications Using Python takes advantage of Python's extensive visualization, simulation, and algorithmic tools to study those topics in nonlinear dynamical systems through numerical algorithms and generated diagrams. After a tutorial introduction to Python, the first part of the book deals with continuous systems using differential equations, including both ordinary and delay differential equations. The second part of the book deals with discrete dynamical systems and progresses to the study of both continuous and discrete systems in contexts like chaos control and synchronization, neural networks, and binary oscillator computing. These later sections are useful reference material for undergraduate student projects. The book is rounded off with example coursework to challenge students' programming abilities and Python-based exam questions. This book will appeal to advanced undergraduate and graduate students, applied mathematicians, engineers, and researchers in a range of disciplines, such as biology, chemistry, computing, economics, and physics. Since it provides a survey of dynamical systems, a familiarity with linear algebra, real and complex analysis, calculus, and ordinary differential equations is necessary, and knowledge of a programming language like C or Java is beneficial but not essential.
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Coding for Beginners: Using Python Usborne Publishing Ltd
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An introduction to coding for complete beginners, this friendly and accessible book will teach children the basics of Python (a widely used programming language), allowing them to get inside the code of their computer and create simple games and animations on screen.
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Computational Physics 3e - Problem Solving with Python Wiley-VCH Verlag GmbH
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The use of computation and simulation has become an essential part of the scientific process. Being able to transform a theory into an algorithm requires significant theoretical insight, detailed physical and mathematical understanding, and a working level of competency in programming.§This upper-division text provides an unusually broad survey of the topics of modern computational physics from a multidisciplinary, computational science point of view. Its philosophy is rooted in learning by doing (assisted by many model programs), with new scientific materials as well as with the Python programming language. Python has become very popular, particularly for physics education and large scientific projects. It is probably the easiest programming language to learn for beginners, yet is also used for mainstream scientific computing, and has packages for excellent graphics and even symbolic manipulations.§The text is designed for an upper-level undergraduate or beginning graduate course and provides the reader with the essential knowledge to understand computational tools and mathematical methods well enough to be successful. As part of the teaching of using computers to solve scientific problems, the reader is encouraged to work through a sample problem stated at the beginning of each chapter or unit, which involves studying the text, writing, debugging and running programs, visualizing the results, and the expressing in words what has been done and what can be concluded. Then there are exercises and problems at the end of each chapter for the reader to work on their own (with model programs given for that purpose).§The text could be used for a one-semester course on scientific computing. The relevant topics for that are covered in the first third of the book. The latter two-thirds of the text includes more physics and can be used for a two-semester course in computational physics, covering nonlinear ODEs, Chaotic Scattering, Fourier Analysis, Wavelet Analysis, Nonlinear Maps, Chaotic systems, Fractals and Parallel Computing.§§The e-book extends the paper version by including many codes, visualizations and applets, as well as links to video lectures.§A table at the beginning of each chapter indicates video lectures, slides, applets and animations.§Applets illustrate the results to be expected for projects in the book, and to help understand some abstract concepts (e.g. Chaotic Scattering)§The eBook's figures, equations, sections, chapters, index, table of contents, code listings, glossary, animations and executable codes (both Applets and Python programs) are linked, much like in a Web document.§Some equations are linked to their xml forms (which can be imported into Maple or Mathematica for manipulation).§The e-book will link to video-based lecture modules, held by principal author Professor Rubin Landau, that cover most every topic in the book.
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Embedded Digital Control with Microcontrollers - Implementation with C and Python John Wiley and Sons Ltd
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EMBEDDED DIGITAL CONTROL WITH MICROCONTROLLERSExplore a concise and practical introduction to implementation methods and the theory of digital control systems on microcontrollersEmbedded Digital Control with Microcontrollers delivers expert instruction in digital control system implementation techniques on the widely used ARM Cortex-M microcontroller. The accomplished authors present the included information in three phases. First, they describe how to implement prototype digital control systems via the Python programming language in order to help the reader better understand theoretical digital control concepts.Second, the book offers readers direction on using the C programming language to implement digital control systems on actual microcontrollers. This will allow readers to solve real-life problems involving digital control, robotics, and mechatronics.Finally, readers will learn how to merge the theoretical and practical issues discussed in the book by implementing digital control systems in real-life applications. Throughout the book, the application of digital control systems using the Python programming language ensures the reader can apply the theory contained within. Readers will also benefit from the inclusion of:* A thorough introduction to the hardware used in the book, including STM32 Nucleo Development Boards and motor drive expansion boards* An exploration of the software used in the book, including Python, MicroPython, and Mbed* Practical discussions of digital control basics, including discrete-time signals, discrete-time systems, linear and time-invariant systems, and constant coefficient difference equations* An examination of how to represent a continuous-time system in digital form, including analog-to-digital conversion and digital-to-analog conversionPerfect for undergraduate students in electrical engineering, Embedded Digital Control with Microcontrollers will also earn a place in the libraries of professional engineers and hobbyists working on digital control and robotics systems seeking a one-stop reference for digital control systems on microcontrollers.
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Beginning Julia Programming APress
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Get started with Julia for engineering and numerical computing, especially data science, machine learning, and scientific computing applications. This book explains how Julia provides the functionality, ease-of-use and intuitive syntax of R, Python, MATLAB, SAS, or Stata combined with the speed, capacity, and performance of C, C++, or Java. You'll learn the OOP principles required to get you started, then how to do basic mathematics with Julia. Other core functionality of Julia that you'll cover, includes working with complex numbers, rational and irrational numbers, rings, and fields. Beginning Julia Programming takes you beyond these basics to harness Julia's powerful features for mathematical functions in Julia, arrays for matrix operations, plotting, and more. Along the way, you also learn how to manage strings, write functions, work with control flows, and carry out I/O to implement and leverage the mathematics needed for your data science and analysis projects. "Julia walks like Python and runs like C". This phrase explains why Julia is quickly growing as the most favored option for data analytics and numerical computation. After reading and using this book, you'll have the essential knowledge and skills to build your first Julia-based application. What You'll Learn Obtain core skills in JuliaApply Julia in engineering and science applicationsWork with mathematical functions in JuliaUse arrays, strings, functions, control flow, and I/O in JuliaCarry out plotting and display basic graphics Who This Book Is For Those who are new to Julia; experienced users may also find this helpful as a reference.
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Building Chatbots with Python APress
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Build your own chatbot using Python and open source tools. This book begins with an introduction to chatbots where you will gain vital information on their architecture. You will then dive straight into natural language processing with the natural language toolkit (NLTK) for building a custom language processing platform for your chatbot. With this foundation, you will take a look at different natural language processing techniques so that you can choose the right one for you. The next stage is to learn to build a chatbot using the API.ai platform and define its intents and entities. During this example, you will learn to enable communication with your bot and also take a look at key points of its integration and deployment. The final chapter of Building Chatbots with Python teaches you how to build, train, and deploy your very own chatbot. Using open source libraries and machine learning techniques you will learn to predict conditions for your bot and develop a conversational agent as a web application. Finally you will deploy your chatbot on your own server with AWS. What You Will Learn Gain the basics of natural language processing using PythonCollect data and train your data for the chatbotBuild your chatbot from scratch as a web appIntegrate your chatbots with Facebook, Slack, and TelegramDeploy chatbots on your own server Who This Book Is For Intermediate Python developers who have no idea about chatbots. Developers with basic Python programming knowledge can also take advantage of the book.
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Ontologies with Python APress
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Use ontologies in Python, with the Owlready2 module developed for ontology-oriented programming. You will start with an introduction and refresher on Python and OWL ontologies. Then, you will dive straight into how to access, create, and modify ontologies in Python. Next, you will move on to an overview of semantic constructs and class properties followed by how to perform automatic reasoning. You will also learn about annotations, multilingual texts, and how to add Python methods to OWL classes and ontologies. Using medical terminologies as well as direct access to RDF triples is also covered. Python is one of the most used programming languages, especially in the biomedical field, and formal ontologies are also widely used. However, there are limited resources for the use of ontologies in Python. Owlready2, downloaded more than 60,000 times, is a response to this problem, and this book is the first one on the topic of using ontologies with Python.What You Will LearnUse Owlready2 to access and modify OWL ontologies in PythonPublish ontologies on dynamic websitesPerform automatic reasoning in PythonUse well-known ontologies, including DBpedia and Gene Ontology, and terminological resources, such as UMLS (Unified Medical Language System)Integrate Python methods in OWL ontologies Who Is This Book ForBeginner to experienced readers from biomedical sciences and artificial intelligence fields would find the book useful.
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C Programming on Raspberry Pi Elektor-Verlag
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The Raspberry Pi has traditionally been programmed using Python. Although this is a very powerful language, many programmers may not be familiar with it. C on the other hand is perhaps the most commonly used programming language and all embedded microcontrollers can be programmed using it.The C language is taught in most technical colleges and universities and almost all engineering students are familiar with using it with their projects. This book is about using the Raspberry Pi with C to develop a range of hardware-based projects. Two of the most popular C libraries, wiringPi and pigpio are used.The book starts with an introduction to C and most students and newcomers will find this chapter invaluable. Many projects are provided in the book, including using Wi-Fi and Bluetooth to establish communication with smartphones.Many sensor and hardware-based projects are included. Both wiringPi and pigpio libraries are used in all projects. Complete program listings are given with full explanations. All projects have been fully tested and work.The following hardware-based projects are provided in the book:Using sensorsUsing LCDsI
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Bayesian Statistical Modeling with Stan, R, and Python Springer, Berlin
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This book provides a highly practical introduction to Bayesian statistical modeling with Stan, which has become the most popular probabilistic programming language.The book is divided into four parts. The first part reviews the theoretical background of modeling and Bayesian inference and presents a modeling workflow that makes modeling more engineering than art. The second part discusses the use of Stan, CmdStanR, and CmdStanPy from the very beginning to basic regression analyses. The third part then introduces a number of probability distributions, nonlinear models, and hierarchical (multilevel) models, which are essential to mastering statistical modeling. It also describes a wide range of frequently used modeling techniques, such as censoring, outliers, missing data, speed-up, and parameter constraints, and discusses how to lead convergence of MCMC. Lastly, the fourth part examines advanced topics for real-world data: longitudinal data analysis, state space models, spatial data analysis, Gaussian processes, Bayesian optimization, dimensionality reduction, model selection, and information criteria, demonstrating that Stan can solve any one of these problems in as little as 30 lines.Using numerous easy-to-understand examples, the book explains key concepts, which continue to be useful when using future versions of Stan and when using other statistical modeling tools. The examples do not require domain knowledge and can be generalized to many fields. The book presents full explanations of code and math formulas, enabling readers to extend models for their own problems. All the code and data are on GitHub.
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