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| mathematica network: Simulating Neural Networks with Mathematica James A. Freeman, 1994 An introduction to neural networks, their operation and their application, in the context of Mathematica, a mathematical programming language. Feature show how to simulate neural network operations using Mathematica and illustrates the techniques for employing Mathematics to assess neural network behaviour and performance. |
| mathematica network: Mathematica Stephen Wolfram, 1991 Just out, the long-waited Release 2.0 of Mathematica. This new edition of the complete reference was released simultaneously and covers all the new features of Release 2.0. Includes a comprehensive review of the increased functionality of the program. Annotation copyrighted by Book News, Inc., Portland, OR |
| mathematica network: Beginning Mathematica and Wolfram for Data Science Jalil Villalobos Alva, 2021 Enhance your data science programming and analysis with the Wolfram programming language and Mathematica. The book will introduce you to the language and its syntax, as well as the structure of Mathematica and its advantages and disadvantages. -- |
| mathematica network: Mathematica Reference Guide Stephen Wolfram, 1992 This authoritative reference guide for Mathematica, Version 2 is designed for convenient reference while users work with the Mathematica program. Mathematicians, scientists, engineers, and programmers using Mathematica will find the reference easy to handle, easy to carry, and packed with essential information. |
| mathematica network: Advances in Network-Embedded Management and Applications Alexander Clemm, Ralf Wolter, 2010-10-28 The general trend of modern network devices towards greater intelligence and programmability is accelerating the development of systems that are increasingly autonomous and to a certain degree self-managing. Examples range from router scripting environments to fully programmable server blades. This has opened up a new field of computer science research, reflected in this new volume. This selection of contributions to the first ever international workshop on network-embedded management applications (NEMA) features six papers selected from submissions to the workshop, held in October 2010 at Niagara Falls, Canada. They represent a wide cross-section of the current work in this vital field of inquiry. Covering a diversity of perspectives, the volume’s dual structure first of all examines the ‘enablers’ for NEMAs—the platforms, frameworks, and development environments which facilitate the evolution of network-embedded management and applications. The second section of the book covers network-embedded applications that might both empower and benefit from such enabling platforms. These papers cover topics ranging from deciding where to best place management control functions inside a network to a discussion of how multi-core hardware processors can be leveraged for traffic filtering applications. The section concludes with an analysis of a delay-tolerant network application in the context of the ‘One Laptop per Child’ program. There is a growing recognition that it is vital to make network operation and administration as easy as possible to contain operational expenses and cope with ever shorter control cycles. This volume provides researchers in the field with the very latest in current thinking. |
| mathematica network: Building Neural Networks David M. Skapura, 1996 Organized by application areas, rather than by specific network architectures or learning algorithms, Building Neural Networks shows why certain networks are more suitable than others for solving specific kinds of problems. Skapura also reviews principles of neural information processing and furnishes an operations summary of the most popular neural-network processing models. |
| mathematica network: Categories for the Working Philosopher Elaine M. Landry, 2017 This is the first volume on category theory for a broad philosophical readership. It is designed to show the interest and significance of category theory for a range of philosophical interests: mathematics, proof theory, computation, cognition, scientific modelling, physics, ontology, the structure of the world. Each chapter is written by either a category-theorist or a philosopher working in one of the represented areas, in an accessible waythat builds on the concepts that are already familiar to philosophers working in these areas. |
| mathematica network: A New Kind of Science Stephen Wolfram, 2018-11-30 NOW IN PAPERBACK€Starting from a collection of simple computer experiments€illustrated in the book by striking computer graphics€Stephen Wolfram shows how their unexpected results force a whole new way of looking at the operation of our universe. |
| mathematica network: Neural Networks Berndt Müller, Joachim Reinhardt, Michael T. Strickland, 1995-10-02 Neural Networks presents concepts of neural-network models and techniques of parallel distributed processing in a three-step approach: - A brief overview of the neural structure of the brain and the history of neural-network modeling introduces to associative memory, preceptrons, feature-sensitive networks, learning strategies, and practical applications. - The second part covers subjects like statistical physics of spin glasses, the mean-field theory of the Hopfield model, and the space of interactions approach to the storage capacity of neural networks. - The final part discusses nine programs with practical demonstrations of neural-network models. The software and source code in C are on a 3 1/2 MS-DOS diskette can be run with Microsoft, Borland, Turbo-C, or compatible compilers. |
| mathematica network: Network Analysis for Management Decisions S.M. Lee, G.L. Moeller, L.A. Digman, 2012-12-06 |
| mathematica network: Mathematics for Machine Learning Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong, 2020-04-23 The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site. |
| mathematica network: Hands-on Start to Wolfram Mathematica Cliff Hastings, Kelvin Mischo, Michael Morrison, 2015 For more than 25 years, Mathematica has been the principal computation environment for millions of innovators, educators, students, and others around the world. This book is an introduction to Mathematica. The goal is to provide a hands-on experience introducing the breadth of Mathematica, with a focus on ease of use. Readers get detailed instruction with examples for interactive learning and end-of-chapter exercises. Each chapter also contains authors tips from their combined 50+ years of Mathematica use. |
| mathematica network: Using Mathematica for Quantum Mechanics Roman Schmied, 2019-09-28 This book revisits many of the problems encountered in introductory quantum mechanics, focusing on computer implementations for finding and visualizing analytical and numerical solutions. It subsequently uses these implementations as building blocks to solve more complex problems, such as coherent laser-driven dynamics in the Rubidium hyperfine structure or the Rashba interaction of an electron moving in 2D. The simulations are highlighted using the programming language Mathematica. No prior knowledge of Mathematica is needed; alternatives, such as Matlab, Python, or Maple, can also be used. |
| mathematica network: Introduction to Queueing Networks J. MacGregor Smith, 2018-08-28 The book examines the performance and optimization of systems where queueing and congestion are important constructs. Both finite and infinite queueing systems are examined. Many examples and case studies are utilized to indicate the breadth and depth of the queueing systems and their range of applicability. Blocking of these processes is very important and the book shows how to deal with this problem in an effective way and not only compute the performance measures of throughput, cycle times, and WIP but also to optimize the resources within these systems. The book is aimed at advanced undergraduate, graduate, and professionals and academics interested in network design, queueing performance models and their optimization. It assumes that the audience is fairly sophisticated in their mathematical understanding, although the explanations of the topics within the book are fairly detailed. |
| mathematica network: Reverse Mathematics John Stillwell, 2019-09-24 This volume presents reverse mathematics to a general mathematical audience for the first time. Stillwell gives a representative view of this field, emphasizing basic analysis--finding the right axioms to prove fundamental theorems--and giving a novel approach to logic. to logic. |
| mathematica network: Inverse Problems For Electrical Networks Edward B Curtis, James A Morrow, 2000-03-02 This book is a very timely exposition of part of an important subject which goes under the general name of “inverse problems”. The analogous problem for continuous media has been very much studied, with a great deal of difficult mathematics involved, especially partial differential equations. Some of the researchers working on the inverse conductivity problem for continuous media (the problem of recovering the conductivity inside from measurements on the outside) have taken an interest in the authors' analysis of this similar problem for resistor networks.The authors' treatment of inverse problems for electrical networks is at a fairly elementary level. It is accessible to advanced undergraduates, and mathematics students at the graduate level. The topics are of interest to mathematicians working on inverse problems, and possibly to electrical engineers. A few techniques from other areas of mathematics have been brought together in the treatment. It is this amalgamation of such topics as graph theory, medial graphs and matrix algebra, as well as the analogy to inverse problems for partial differential equations, that makes the book both original and interesting. |
| mathematica network: Metamathematics and the Philosophical Tradition William Boos, 2018-12-17 Metamathematics and the Philosophical Tradition is the first work to explore in such historical depth the relationship between fundamental philosophical quandaries regarding self-reference and meta-mathematical notions of consistency and incompleteness. Using the insights of twentieth-century logicians from Gödel through Hilbert and their successors, this volume revisits the writings of Aristotle, the ancient skeptics, Anselm, and enlightenment and seventeenth and eighteenth century philosophers Leibniz, Berkeley, Hume, Pascal, Descartes, and Kant to identify ways in which these both encode and evade problems of a priori definition and self-reference. The final chapters critique and extend more recent insights of late 20th-century logicians and quantum physicists, and offer new applications of the completeness theorem as a means of exploring metatheoretical ascent and the limitations of scientific certainty. Broadly syncretic in range, Metamathematics and the Philosophical Tradition addresses central and recurring problems within epistemology. The volume’s elegant, condensed writing style renders accessible its wealth of citations and allusions from varied traditions and in several languages. Its arguments will be of special interest to historians and philosophers of science and mathematics, particularly scholars of classical skepticism, the Enlightenment, Kant, ethics, and mathematical logic. |
| mathematica network: Functional Networks with Applications Enrique Castillo, Angel Cobo, Jose Antonio Gutierrez, Rosa Eva Pruneda, 2012-12-06 Artificial neural networks have been recognized as a powerful tool to learn and reproduce systems in various fields of applications. Neural net works are inspired by the brain behavior and consist of one or several layers of neurons, or computing units, connected by links. Each artificial neuron receives an input value from the input layer or the neurons in the previ ous layer. Then it computes a scalar output from a linear combination of the received inputs using a given scalar function (the activation function), which is assumed the same for all neurons. One of the main properties of neural networks is their ability to learn from data. There are two types of learning: structural and parametric. Structural learning consists of learning the topology of the network, that is, the number of layers, the number of neurons in each layer, and what neurons are connected. This process is done by trial and error until a good fit to the data is obtained. Parametric learning consists of learning the weight values for a given topology of the network. Since the neural functions are given, this learning process is achieved by estimating the connection weights based on the given information. To this aim, an error function is minimized using several well known learning methods, such as the backpropagation algorithm. Unfortunately, for these methods: (a) The function resulting from the learning process has no physical or engineering interpretation. Thus, neural networks are seen as black boxes. |
| mathematica network: Mathematica for Physics Robert L. Zimmerman, Fredrick Iver Olness, 1995-01 Mathematica is a mathematical software system for researchers, students and anyone seeking an effective tool for mathematical analysis. This text aims to help readers learn the software in the context of solving physics problems. The graphical capabilities of Mathematica are emphasized and the readers are encouraged to use their intuition for the physics behind the problem. |
| mathematica network: Mathematica Navigator Heikki Ruskeepaa, Heikki Ruskeepää, 2004-02-06 Mathematica Navigator gives you a general introduction to Mathematica. The book emphasizes graphics, methods of applied mathematics and statistics, and programming. Mathematica Navigator can be used both as a tutorial and as a handbook. While no previous experience with Mathematica is required, most chapters also include advanced material, so that the book will be a valuable resource for both beginners and experienced users. |
| mathematica network: CAEN Newsletter University of Michigan. Computer Aided Engineering Network, 1994 |
| mathematica network: Reaction Kinetics: Exercises, Programs and Theorems János Tóth, Attila László Nagy, Dávid Papp, 2018-09-18 Fifty years ago, a new approach to reaction kinetics began to emerge: one based on mathematical models of reaction kinetics, or formal reaction kinetics. Since then, there has been a rapid and accelerated development in both deterministic and stochastic kinetics, primarily because mathematicians studying differential equations and algebraic geometry have taken an interest in the nonlinear differential equations of kinetics, which are relatively simple, yet capable of depicting complex behavior such as oscillation, chaos, and pattern formation. The development of stochastic models was triggered by the fact that novel methods made it possible to measure molecules individually. Now it is high time to make the results of the last half-century available to a larger audience: students of chemistry, chemical engineering and biochemistry, not to mention applied mathematics. Based on recent papers, this book presents the most important concepts and results, together with a wealth of solved exercises. The book is accompanied by the authors’ Mathematica package, ReactionKinetics, which helps both students and scholars in their everyday work, and which can be downloaded from http://extras.springer.com/ and also from the authors’ websites. Further, the large set of unsolved problems provided may serve as a springboard for individual research. |
| mathematica network: Bayesian Nonparametrics via Neural Networks Herbert K. H. Lee, 2004-01-01 Bayesian Nonparametrics via Neural Networks is the first book to focus on neural networks in the context of nonparametric regression and classification, working within the Bayesian paradigm. Its goal is to demystify neural networks, putting them firmly in a statistical context rather than treating them as a black box. This approach is in contrast to existing books, which tend to treat neural networks as a machine learning algorithm instead of a statistical model. Once this underlying statistical model is recognized, other standard statistical techniques can be applied to improve the model. The Bayesian approach allows better accounting for uncertainty. This book covers uncertainty in model choice and methods to deal with this issue, exploring a number of ideas from statistics and machine learning. A detailed discussion on the choice of prior and new noninformative priors is included, along with a substantial literature review. Written for statisticians using statistical terminology, Bayesian Nonparametrics via Neural Networks will lead statisticians to an increased understanding of the neural network model and its applicability to real-world problems. |
| mathematica network: No-Nonsense Resumes Arnold G. Boldt, Wendy S. Enelow, 2006-11-15 For the first time ever, here’s a resume book that clears away the clutter and gets down to the “brass tacks” of what it takes to write and design a resume that will get you interviews and job offers. Authors and professional resume writers Wendy Enelow and Arnold Boldt share their insights, knowledge, and more than 35 years of combined experience to help you prepare a resume that will get you noticed, not passed over. No-Nonsense Resumes begins with a thorough but easy-to-understand explanation of the key elements that are vital to creating an “attention-grabbing” resume, including how to: — Strategically “position” your resume — Showcase your skills and achievements — Format and design a professional-looking resume — Select and integrate key words — Prepare and distribute your electronic resume Subsequent chapters offer specific tips on creating winning resumes for job opportunities in virtually every profession: Administration & Clerical; Accounting, Banking & Finance; Government; Health Care & Social Services; Hospitality Management & Food Service; Human Resources & Training; Law Enforcement & Legal; Manufacturing & Operations; Sales, Marketing & Customer Service; Skilled Trades; and Technology, Science & Engineering. Included in each chapter are sample resumes contributed by leading resume writers and career consultants worldwide. |
| mathematica network: FPGA Implementations of Neural Networks Amos R. Omondi, Jagath C. Rajapakse, 2006-10-04 During the 1980s and early 1990s there was signi?cant work in the design and implementation of hardware neurocomputers. Nevertheless, most of these efforts may be judged to have been unsuccessful: at no time have have ha- ware neurocomputers been in wide use. This lack of success may be largely attributed to the fact that earlier work was almost entirely aimed at developing custom neurocomputers, based on ASIC technology, but for such niche - eas this technology was never suf?ciently developed or competitive enough to justify large-scale adoption. On the other hand, gate-arrays of the period m- tioned were never large enough nor fast enough for serious arti?cial-neur- network (ANN) applications. But technology has now improved: the capacity and performance of current FPGAs are such that they present a much more realistic alternative. Consequently neurocomputers based on FPGAs are now a much more practical proposition than they have been in the past. This book summarizes some work towards this goal and consists of 12 papers that were selected, after review, from a number of submissions. The book is nominally divided into three parts: Chapters 1 through 4 deal with foundational issues; Chapters 5 through 11 deal with a variety of implementations; and Chapter 12 looks at the lessons learned from a large-scale project and also reconsiders design issues in light of current and future technology. |
| mathematica network: Software Engineering and Knowledge Engineering: Theory and Practice Yanwen Wu, 2012-02-01 The volume includes a set of selected papers extended and revised from the I2009 Pacific-Asia Conference on Knowledge Engineering and Software Engineering (KESE 2009) was held on December 19~ 20, 2009, Shenzhen, China. Volume 2 is to provide a forum for researchers, educators, engineers, and government officials involved in the general areas of Knowledge Engineering and Communication Technology to disseminate their latest research results and exchange views on the future research directions of these fields. 135 high-quality papers are included in the volume. Each paper has been peer-reviewed by at least 2 program committee members and selected by the volume editor Prof.Yanwen Wu. On behalf of the this volume, we would like to express our sincere appreciation to all of authors and referees for their efforts reviewing the papers. Hoping you can find lots of profound research ideas and results on the related fields of Knowledge Engineering and Communication Technology. |
| mathematica network: Network Scheduling Techniques for Construction Project Management M. Hajdu, 2013-03-09 Industrial, financial, commercial or any kinds of project have at least one common feature: the better organized they are, the higher the profit or the lower the cost. Project management is the principle of planning different projects and keeping them on track within time, cost and resource constraints. The need for effective project management is ever-increasing. The complexity of the environment we live in requires more sophisticated methods than it did just a couple of decades ago. Project managers might face insurmountable obstacles in their work if they do not adapt themselves to the changing circumstances. On the other hand, better knowledge of project management can result in better plans, schedules and, last but not least, more contracts and more profit. This knowledge can help individuals and firms to stay alive in this competitive market and, in the global sense, utilize the finite resources of our planet in a more efficient way. |
| mathematica network: Information Technology Digest , 1994 |
| mathematica network: Computational Science - ICCS 2007 Yong Shi, 2007-05-18 Part of a four-volume set, this book constitutes the refereed proceedings of the 7th International Conference on Computational Science, ICCS 2007, held in Beijing, China in May 2007. The papers cover a large volume of topics in computational science and related areas, from multiscale physics to wireless networks, and from graph theory to tools for program development. |
| mathematica network: Complex Physical, Biophysical and Econophysical Systems Robert L. Dewar, Frank Detering, 2010 1. Introduction to complex and econophysics systems : a navigation map / T. Aste and T. Di Matteo -- 2. An introduction to fractional diffusion / B. I. Henry, T.A.M. Langlands and P. Straka -- 3. Space plasmas and fusion plasmas as complex systems / R. O. Dendy -- 4. Bayesian data analysis / M. S. Wheatland -- 5. Inverse problems and complexity in earth system science / I. G. Enting -- 6. Applied fluid chaos : designing advection with periodically reoriented flows for micro to geophysical mixing and transport enhancement / G. Metcalfe -- 7. Approaches to modelling the dynamical activity of brain function based on the electroencephalogram / D. T. J. Liley and F. Frascoli -- 8. Jaynes' maximum entropy principle, Riemannian metrics and generalised least action bound / R. K. Niven and B. Andresen -- 9. Complexity, post-genomic biology and gene expression programs / R. B. H. Williams and O. J.-H. Luo -- 10. Tutorials on agent-based modelling with NetLogo and network analysis with Pajek / M. J. Berryman and S. D. Angus. |
| mathematica network: Resource Management of Mobile Cloud Computing Networks and Environments Mastorakis, George, Mavromoustakis, Constandinos X., Pallis, Evangelos, 2015-03-31 As more and more of our data is stored remotely, accessing that data wherever and whenever it is needed is a critical concern. More concerning is managing the databanks and storage space necessary to enable cloud systems. Resource Management of Mobile Cloud Computing Networks and Environments reports on the latest advances in the development of computationally intensive and cloud-based applications. Covering a wide range of problems, solutions, and perspectives, this book is a scholarly resource for specialists and end-users alike making use of the latest cloud technologies. |
| mathematica network: Introduction to Probability with Mathematica Kevin J. Hastings, 2009-09-21 Updated to conform to Mathematica® 7.0, Introduction to Probability with Mathematica®, Second Edition continues to show students how to easily create simulations from templates and solve problems using Mathematica. It provides a real understanding of probabilistic modeling and the analysis of data and encourages the application of these ideas to practical problems. The accompanyingdownloadable resources offer instructors the option of creating class notes, demonstrations, and projects. New to the Second Edition Expanded section on Markov chains that includes a study of absorbing chains New sections on order statistics, transformations of multivariate normal random variables, and Brownian motion More example data of the normal distribution More attention on conditional expectation, which has become significant in financial mathematics Additional problems from Actuarial Exam P New appendix that gives a basic introduction to Mathematica New examples, exercises, and data sets, particularly on the bivariate normal distribution New visualization and animation features from Mathematica 7.0 Updated Mathematica notebooks on the downloadable resources. After covering topics in discrete probability, the text presents a fairly standard treatment of common discrete distributions. It then transitions to continuous probability and continuous distributions, including normal, bivariate normal, gamma, and chi-square distributions. The author goes on to examine the history of probability, the laws of large numbers, and the central limit theorem. The final chapter explores stochastic processes and applications, ideal for students in operations research and finance. |
| mathematica network: Information Theory and Network Coding Raymond W. Yeung, 2008-08-28 This book is an evolution from my book A First Course in Information Theory published in 2002 when network coding was still at its infancy. The last few years have witnessed the rapid development of network coding into a research ?eld of its own in information science. With its root in infor- tion theory, network coding has not only brought about a paradigm shift in network communications at large, but also had signi?cant in?uence on such speci?c research ?elds as coding theory, networking, switching, wireless c- munications,distributeddatastorage,cryptography,andoptimizationtheory. While new applications of network coding keep emerging, the fundamental - sults that lay the foundation of the subject are more or less mature. One of the main goals of this book therefore is to present these results in a unifying and coherent manner. While the previous book focused only on information theory for discrete random variables, the current book contains two new chapters on information theory for continuous random variables, namely the chapter on di?erential entropy and the chapter on continuous-valued channels. With these topics included, the book becomes more comprehensive and is more suitable to be used as a textbook for a course in an electrical engineering department. |
| mathematica network: Macro-Econophysics Hideaki Aoyama, Yoshi Fujiwara, Yuichi Ikeda, Hiroshi Iyetomi, Wataru Souma, Hiroshi Yoshikawa, 2017-07-04 The concepts of statistical physics and big data play an important role in the evidence-based analysis and interpretation of macroeconomic principles. The techniques of complex networks, big data, and statistical physics are useful to understand theories of economic systems, and the authors have applied these to understand the intricacies of complex macroeconomic problems. Recent research work using tools and techniques of big data, statistical physics, complex networks, and statistical science is covered, and basic graph algorithms and statistical measures of complex networks are described. The application of big data and statistical physics tools to assess price dynamics, inflation, systemic risks, and productivity is discussed. Chapter-end summary and numerical problems are provided to reinforce understanding of concepts. |
| mathematica network: BIOMAT 2005 Rubem Mondaini, Rui Dil?o, 2006 This volume contains the contributions of the keynote speakers to the BIOMAT 2005 symposium, as well as a collection of selected papers by pioneering researchers. It provides a comprehensive review of the mathematical modeling of cancer development, Alzheimer's disease, malaria, and aneurysm development. Various models for the immune system and epidemiological issues are analyzed and reviewed. The book also explores protein structure prediction by optimization and combinatorial techniques (Steiner trees). The coverage includes bioinformatics issues, regulation of gene expression, evolution, development, DNA and array modeling, and small world networks. |
| mathematica network: Biomat 2005 - Proceedings Of The International Symposium On Mathematical And Computational Biology Rubem P Mondaini, Rui Dilao, 2006-04-25 This volume contains the contributions of the keynote speakers to the BIOMAT 2005 symposium, as well as a collection of selected papers by pioneering researchers. It provides a comprehensive review of the mathematical modeling of cancer development, Alzheimer's disease, malaria, and aneurysm development. Various models for the immune system and epidemiological issues are analyzed and reviewed. The book also explores protein structure prediction by optimization and combinatorial techniques (Steiner trees). The coverage includes bioinformatics issues, regulation of gene expression, evolution, development, DNA and array modeling, and small world networks. |
| mathematica network: The Calabi–Yau Landscape Yang-Hui He, 2021-07-31 Can artificial intelligence learn mathematics? The question is at the heart of this original monograph bringing together theoretical physics, modern geometry, and data science. The study of Calabi–Yau manifolds lies at an exciting intersection between physics and mathematics. Recently, there has been much activity in applying machine learning to solve otherwise intractable problems, to conjecture new formulae, or to understand the underlying structure of mathematics. In this book, insights from string and quantum field theory are combined with powerful techniques from complex and algebraic geometry, then translated into algorithms with the ultimate aim of deriving new information about Calabi–Yau manifolds. While the motivation comes from mathematical physics, the techniques are purely mathematical and the theme is that of explicit calculations. The reader is guided through the theory and provided with explicit computer code in standard software such as SageMath, Python and Mathematica to gain hands-on experience in applications of artificial intelligence to geometry. Driven by data and written in an informal style, The Calabi–Yau Landscape makes cutting-edge topics in mathematical physics, geometry and machine learning readily accessible to graduate students and beyond. The overriding ambition is to introduce some modern mathematics to the physicist, some modern physics to the mathematician, and machine learning to both. |
| mathematica network: Soft Computing in Textile Engineering Abhijit Majumdar, 2010-11-29 Soft computing refers to a collection of computational techniques which study, model and analyse complex phenomena. As many textile engineering problems are inherently complex in nature, soft computing techniques have often provided optimum solutions to these cases. Although soft computing has several facets, it mainly revolves around three techniques; artificial neural networks, fuzzy logic and genetic algorithms. The book is divided into five parts, covering the entire process of textile production, from fibre manufacture to garment engineering. These include soft computing techniques in yarn manufacture and modelling, fabric and garment manufacture, textile properties and applications and textile quality evaluation. - Covers the entire process of textile production, from fibre manufacture to garment engineering including artificial neural networks, fuzzy logic and genetic algorithms - Examines soft computing techniques in yarn manufacture and modelling, fabric and garment manufacture - Specifically reviews soft computing in relation to textile properties and applications featuring garment modelling and sewing machines |
| mathematica network: Shortest Path Network Problems Jin Y. Yen, 1975 |
| mathematica network: Philosophy and Model Theory Tim Button, Sean P. Walsh, 2018 Model theory is used in every theoretical branch of analytic philosophy: in philosophy of mathematics, in philosophy of science, in philosophy of language, in philosophical logic, and in metaphysics. But these wide-ranging uses of model theory have created a highly fragmented literature. On the one hand, many philosophically significant results are found only in mathematics textbooks: these are aimed squarely at mathematicians; they typically presuppose that the reader has a serious background in mathematics; and little clue is given as to their philosophical significance. On the other hand, the philosophical applications of these results are scattered across disconnected pockets of papers. The first aim of this book, then, is to explore the philosophical uses of model theory, focusing on the central topics of reference, realism, and doxology. Its second aim is to address important questions in the philosophy of model theory, such as: sameness of theories and structure, the boundaries of logic, and the classification of mathematical structures. Philosophy and Model Theory will be accessible to anyone who has completed an introductory logic course. It does not assume that readers have encountered model theory before, but starts right at the beginning, discussing philosophical issues that arise even with conceptually basic model theory. Moreover, the book is largely self-contained: model-theoretic notions are defined as and when they are needed for the philosophical discussion, and many of the most philosophically significant results are given accessible proofs. |
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Wolfram Mathematica: Modern Technical Computing
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Wolfram Mathematica Online: Bring Mathematica to Life in the …
Mathematica Online brings the world's ultimate computation system to the modern cloud environment. Use the power of Mathematica interactive notebooks to work directly in your web …
Mathematica Student Edition: Computation Help for Math, Science ...
Any Subject, Any Level You can use Mathematica Student Edition to explore any topic—regardless of differences in textbooks, knowledge levels or teaching styles. You'll save …
Wolfram Mathematica Personal Edition
Data and computation tool for your hobbies and interests. Compute, track, model, program, document. Full power of Mathematica at personal-use price.
Download a Free Trial of Mathematica - Wolfram
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Latest Features in Mathematica 14 - Wolfram
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Mathematica License Pricing Options - Wolfram
Prices for commercial, non-profit, government, education, home & student Mathematica use. Also, service plans, upgrades, networks, sites, private cloud.
Mathematica Resources: Learning Tools, Examples, Training
Check out our collection of anything Mathematica users need: videos, tutorials, books, documentation, demos, training, forums, free seminars, educational materials, and more.
Wolfram: Computation Meets Knowledge
Launching Version 14.2 of Wolfram Language & Mathematica: Big Data Meets Computation & AI
Latest Features in Mathematica 13 - Wolfram
Details about featured Mathematica 13 functionality: symbolic & numeric computations, visualization & graphics, geometry & geography, data science & computation, image & audio, …
