Stephen Lynch - Böcker

In reshaping Lodge's Rosalynde into As You Like It, Shakespeare not only undermines the Petrarchan and pastoral traditions of the romance, but also refutes the implicit gender structures upon which such Petrarchanisms are based. In refashioning The True Chronicle Historie of King Leir into the tragedy of King Lear, Shakespeare does not simply reject the explicit Christian setting and happy ending of Leir, but engages and responds to the highly Reformational and Calvinistic assumptions that shape and inform the source play. In rewriting Greene's Pandosto into The Winter's Tale, Shakespeare not only adapts the plot and characterization of the source, but consistently counters and refutes the rhetorical and linguistic structures of Greene's romance. And in Pericles, Shakespeare adapts the Appolinus story from Gower's Confessio Amantis, but also responds to suggestions in the source text about the authority of the role of the author.

This comprehensive guide includes a discussion of the play's textual history, analyses of its various contexts and sources of influence, an examination of its dramatic structure, a detailed plot summary, a discussion of major themes and critical approaches, and a look at major productions from the 16th through the 20th century.Shakespeare's As You Like It continues to captivate audiences some 400 years after it was written. This reference is a comprehensive guide to the play. Beginning with a discussion of the play's textual history, the guide analyzes its various contexts and sources of influence. The play's dramatic structure is examined, a detailed plot summary is offered, and the play's major themes—along with an overview of major critical approaches to the work—are discussed. The volume also covers major productions of As You Like It from the 16th through the 20th century.

Since the ?rst edition of this book was published in 2001, the algebraic computa- ™ tion package Maple has evolved from Maple V into Maple 13. Accordingly, the second edition has been thoroughly updated and new material has been added. In this edition, there are many more applications, examples, and exercises, all with solutions, and new chapters on neural networks and simulation have been added. Therearealsonewsectionsonperturbationmethods,normalforms,Gröbnerbases, and chaos synchronization. This book provides an introduction to the theory of dynamical systems with the aid of the Maple algebraic manipulation package. It is written for both senior undergraduates and graduate students. The ?rst part of the book deals with c- tinuous systems using ordinary differential equations (Chapters 1–10 ), the second part is devoted to the study of discrete dynamical systems (Chapters 11–15), and Chapters 16–18 deal with both continuous and discrete systems. Chapter 19 lists examination-type questions used by the author over many years, one set to be used in a computer laboratory with access to Maple, and the other set to be used without access to Maple. Chapter 20 lists answers to all of the exercises given in the book. It should be pointed out that dynamical systems theory is not l- ited to these topics but also encompasses partial differential equations, integral and integro-differential equations, stochastic systems, and time delay systems, for instance. References [1]–[5] given at the end of the Preface provide more inf- mation for the interested reader.

Python for Scientific Computing and Artificial Intelligence is split into 3 parts: in Section 1, the reader is introduced to the Python programming language and shown how Python can aid in the understanding of advanced High School Mathematics. In Section 2, the reader is shown how Python can be used to solve real-world problems from a broad range of scientific disciplines. Finally, in Section 3, the reader is introduced to neural networks and shown how TensorFlow (written in Python) can be used to solve a large array of problems in Artificial Intelligence (AI).This book was developed from a series of national and international workshops that the author has been delivering for over twenty years. The book is beginner friendly and has a strong practical emphasis on programming and computational modelling.Features:No prior experience of programming is requiredOnline GitHub repository available with codes for readers to practiceCovers applications and examples from biology, chemistry, computer science, data science, electrical and mechanical engineering, economics, mathematics, physics, statistics and binary oscillator computingFull solutions to exercises are available as Jupyter notebooks on the WebSupport MaterialGitHub Repository of Python Files and Notebooks: https://github.com/proflynch/CRC-Press/Solutions to All Exercises:Section 1: An Introduction to Python: https://drstephenlynch.github.io/webpages/Solutions_Section_1.htmlSection 2: Python for Scientific Computing: https://drstephenlynch.github.io/webpages/Solutions_Section_2.htmlSection 3: Artificial Intelligence: https://drstephenlynch.github.io/webpages/Solutions_Section_3.html

Python for Scientific Computing and Artificial Intelligence is split into 3 parts: in Section 1, the reader is introduced to the Python programming language and shown how Python can aid in the understanding of advanced High School Mathematics. In Section 2, the reader is shown how Python can be used to solve real-world problems from a broad range of scientific disciplines. Finally, in Section 3, the reader is introduced to neural networks and shown how TensorFlow (written in Python) can be used to solve a large array of problems in Artificial Intelligence (AI).This book was developed from a series of national and international workshops that the author has been delivering for over twenty years. The book is beginner friendly and has a strong practical emphasis on programming and computational modelling.Features:No prior experience of programming is requiredOnline GitHub repository available with codes for readers to practiceCovers applications and examples from biology, chemistry, computer science, data science, electrical and mechanical engineering, economics, mathematics, physics, statistics and binary oscillator computingFull solutions to exercises are available as Jupyter notebooks on the WebSupport MaterialGitHub Repository of Python Files and Notebooks: https://github.com/proflynch/CRC-Press/Solutions to All Exercises:Section 1: An Introduction to Python: https://drstephenlynch.github.io/webpages/Solutions_Section_1.htmlSection 2: Python for Scientific Computing: https://drstephenlynch.github.io/webpages/Solutions_Section_2.htmlSection 3: Artificial Intelligence: https://drstephenlynch.github.io/webpages/Solutions_Section_3.html

A Simple Introduction to Python is aimed at pre-university students and complete novices to programming. The whole book has been created using Jupyter notebooks. After introducing Python as a powerful calculator, simple programming constructs are covered, and the NumPy, MatPlotLib and SymPy modules (libraries) are introduced. Python is then used for Mathematics, Cryptography, Artificial Intelligence, Data Science and Object Oriented Programming.The reader is shown how to program using the integrated development environments: Python IDLE, Spyder, Jupyter notebooks, and through cloud computing with Google Colab.Features:No prior experience in programming is required.Demonstrates how to format Jupyter notebooks for publication on the Web.Full solutions to exercises are available as a Jupyter notebook on the Web.All Jupyter notebook solution files can be downloaded through GitHub.GitHub Repository of Data Files and a Jupyter Solution notebook: https://github.com/proflynch/A-Simple-Introduction-to-PythonJupyter Solution notebook web page: https://drstephenlynch.github.io/webpages/A-Simple-Introduction-to-Python-Solutions.html

A Simple Introduction to Python is aimed at pre-university students and complete novices to programming. The whole book has been created using Jupyter notebooks. After introducing Python as a powerful calculator, simple programming constructs are covered, and the NumPy, MatPlotLib and SymPy modules (libraries) are introduced. Python is then used for Mathematics, Cryptography, Artificial Intelligence, Data Science and Object Oriented Programming.The reader is shown how to program using the integrated development environments: Python IDLE, Spyder, Jupyter notebooks, and through cloud computing with Google Colab.Features:No prior experience in programming is required.Demonstrates how to format Jupyter notebooks for publication on the Web.Full solutions to exercises are available as a Jupyter notebook on the Web.All Jupyter notebook solution files can be downloaded through GitHub.GitHub Repository of Data Files and a Jupyter Solution notebook: https://github.com/proflynch/A-Simple-Introduction-to-PythonJupyter Solution notebook web page: https://drstephenlynch.github.io/webpages/A-Simple-Introduction-to-Python-Solutions.html

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.

This textbook, now in its third edition, provides a broad and accessible introduction to both continuous and discrete dynamical systems, the theory of which is motivated by examples from a wide range of disciplines. It emphasizes applications and simulation utilizing MATLAB®, Simulink®, the Image Processing Toolbox®, the Symbolic Math Toolbox®, and the Deep Learning Toolbox®.The text begins with a tutorial introduction to MATLAB that assumes no prior programming knowledge.  Discrete systems are covered in the first part, after which the second part explores the study of continuous systems using delay, ordinary, and partial differential equations.  The third part considers chaos control and synchronization, binary oscillator computing, Simulink, and the Deep Learning Toolbox.  A final chapter provides examination- and coursework-type MATLAB questions for use by instructors and students.  For the Third Edition, all the material has been thoroughly updated in line with the most recent version of MATLAB, R2025a.  New chapters have been added on artificial neural networks, delay differential equations, numerical methods for ordinary and partial differential equations, and the Deep Learning Toolbox.  MATLAB program files, Simulink model files, and other materials are available to download from the author’s website and through GitHub.The hands-on approach of Dynamical Systems with Applications using MATLAB® has minimal prerequisites, only requiring familiarity with ordinary differential equations. It will appeal to advanced undergraduate and graduate students, applied mathematicians, engineers, and researchers in a broad range of disciplines such as population dynamics, biology, chemistry, computing, economics, nonlinear optics, neural networks, and physics.Praise for the Second Edition:“This book [is] a valuable reference to the existing literature on dynamical systems, especially for the remarkable collection of examples and applications selected from very different areas, as well as for its treatment with MATLAB of these problems.” -- Fernando Casas, zbMATH“[The] vast compilation of applications makes this text a great resource for applied mathematicians, engineers, physicists, and researchers. Instructors will be pleased to find an aims and objectives section at the beginning of each chapter where the author outlines its content and provides student learning objectives.” -- Stanley R. Huddy, MAA Reviews

This book provides an introduction to the theory of dynamical systems with the aid of the Mathematica(R) computer algebra package. The book has a very hands-on approach and takes the reader from basic theory to recently published research material.

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.