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Applied Data Science with Python and Jupyter: Use powerful industry-standard tools to unlock new, actionable insights from your data
by Alex GaleaBecome the master player of data exploration by creating reproducible data processing pipelines, visualizations, and prediction models for your applications.Key FeaturesGet up and running with the Jupyter ecosystem and some example datasetsLearn about key machine learning concepts such as SVM, KNN classifiers, and Random ForestsDiscover how you can use web scraping to gather and parse your own bespoke datasetsBook DescriptionGetting started with data science doesn't have to be an uphill battle. Applied Data Science with Python and Jupyter is a step-by-step guide ideal for beginners who know a little Python and are looking for a quick, fast-paced introduction to these concepts. In this book, you'll learn every aspect of the standard data workflow process, including collecting, cleaning, investigating, visualizing, and modeling data. You'll start with the basics of Jupyter, which will be the backbone of the book. After familiarizing ourselves with its standard features, you'll look at an example of it in practice with our first analysis. In the next lesson, you dive right into predictive analytics, where multiple classification algorithms are implemented. Finally, the book ends by looking at data collection techniques. You'll see how web data can be acquired with scraping techniques and via APIs, and then briefly explore interactive visualizations.What you will learnGet up and running with the Jupyter ecosystemIdentify potential areas of investigation and perform exploratory data analysisPlan a machine learning classification strategy and train classification modelsUse validation curves and dimensionality reduction to tune and enhance your modelsScrape tabular data from web pages and transform it into Pandas DataFramesCreate interactive, web-friendly visualizations to clearly communicate your findingsWho this book is forApplied Data Science with Python and Jupyter is ideal for professionals with a variety of job descriptions across a large range of industries, given the rising popularity and accessibility of data science. You'll need some prior experience with Python, with any prior work with libraries such as Pandas, Matplotlib, and Pandas providing you a useful head start.
The Applied Data Science Workshop: Get started with the applications of data science and techniques to explore and assess data effectively, 2nd Edition
by Alex GaleaDesigned with beginners in mind, this workshop helps you make the most of Python libraries and the Jupyter Notebook's functionality to understand how data science can be applied to solve real-world data problems. Key Features Gain useful insights into data science and machine learning Explore the different functionalities and features of a Jupyter Notebook Discover how Python libraries are used with Jupyter for data analysis Book Description From banking and manufacturing through to education and entertainment, using data science for business has revolutionized almost every sector in the modern world. It has an important role to play in everything from app development to network security. Taking an interactive approach to learning the fundamentals, this book is ideal for beginners. You'll learn all the best practices and techniques for applying data science in the context of real-world scenarios and examples. Starting with an introduction to data science and machine learning, you'll start by getting to grips with Jupyter functionality and features. You'll use Python libraries like sci-kit learn, pandas, Matplotlib, and Seaborn to perform data analysis and data preprocessing on real-world datasets from within your own Jupyter environment. Progressing through the chapters, you'll train classification models using sci-kit learn, and assess model performance using advanced validation techniques. Towards the end, you'll use Jupyter Notebooks to document your research, build stakeholder reports, and even analyze web performance data. By the end of The Applied Data Science Workshop, you'll be prepared to progress from being a beginner to taking your skills to the next level by confidently applying data science techniques and tools to real-world projects. What you will learn Understand the key opportunities and challenges in data science Use Jupyter for data science tasks such as data analysis and modeling Run exploratory data analysis within a Jupyter Notebook Visualize data with pairwise scatter plots and segmented distribution Assess model performance with advanced validation techniques Parse HTML responses and analyze HTTP requests Who this book is for If you are an aspiring data scientist who wants to build a career in data science or a developer who wants to explore the applications of data science from scratch and analyze data in Jupyter using Python libraries, then this book is for you. Although a brief understanding of Python programming and machine learning is recommended to help you grasp the topics covered in the book more quickly, it is not mandatory.
Applied Data Visualization with R and ggplot2: Create useful, elaborate, and visually appealing plots
by Dr Tania MoulikDevelop informative and aesthetic visualizations that enable effective data analysis in less timeKey FeaturesDiscover structure of ggplot2, grammar of graphics, and geometric objectsStudy how to design and implement visualization from scratchExplore the advantages of using advanced plotsBook DescriptionApplied Data Visualization with R and ggplot2 introduces you to the world of data visualization by taking you through the basic features of ggplot2. To start with, you’ll learn how to set up the R environment, followed by getting insights into the grammar of graphics and geometric objects before you explore the plotting techniques.You’ll discover what layers, scales, coordinates, and themes are, and study how you can use them to transform your data into aesthetical graphs. Once you’ve grasped the basics, you’ll move on to studying simple plots such as histograms and advanced plots such as superimposing and density plots. You’ll also get to grips with plotting trends, correlations, and statistical summaries.By the end of this book, you’ll have created data visualizations that will impress your clients.What you will learnSet up the R environment, RStudio, and understand structure of ggplot2Distinguish variables and use best practices to visualize themChange visualization defaults to reveal more information about dataImplement the grammar of graphics in ggplot2 such as scales and facetingBuild complex and aesthetic visualizations with ggplot2 analysis methodsLogically and systematically explore complex relationshipsCompare variables in a single visual, with advanced plotting methodsWho this book is forApplied Data Visualization with R and ggplot2 is for you if you are a professional working with data and R. This book is also for students who want to enhance their data analysis skills by adding informative and professional visualizations. It is assumed that you know basics of the R language and its commands and objects.
Applied Decision-Making: Applications in Computer Sciences and Engineering (Studies in Systems, Decision and Control #209)
by Mauricio A. Sanchez Leocundo Aguilar Manuel Castañón-Puga Antonio RodríguezThis book gathers a collection of the latest research, applications, and proposals, introducing readers to innovations and concepts from diverse environments and systems. As such, it will provide students and professionals alike with not only cutting-edge information, but also new inspirations and potential research directions.Each chapter focuses on a specific aspect of applied decision making, e.g. in complex systems, computational intelligence, security, and ubiquitous computing.
Applied Deep Learning: Tools, Techniques, and Implementation (Computational Intelligence Methods and Applications)
by Paul Fergus Carl ChalmersThis book focuses on the applied aspects of artificial intelligence using enterprise frameworks and technologies. The book is applied in nature and will equip the reader with the necessary skills and understanding for delivering enterprise ML technologies. It will be valuable for undergraduate and postgraduate students in subjects such as artificial intelligence and data science, and also for industrial practitioners engaged with data analytics and machine learning tasks. The book covers all of the key conceptual aspects of the field and provides a foundation for all interested parties to develop their own artificial intelligence applications.
Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks
by Umberto MichelucciWork with advanced topics in deep learning, such as optimization algorithms, hyper-parameter tuning, dropout, and error analysis as well as strategies to address typical problems encountered when training deep neural networks. You’ll begin by studying the activation functions mostly with a single neuron (ReLu, sigmoid, and Swish), seeing how to perform linear and logistic regression using TensorFlow, and choosing the right cost function. The next section talks about more complicated neural network architectures with several layers and neurons and explores the problem of random initialization of weights. An entire chapter is dedicated to a complete overview of neural network error analysis, giving examples of solving problems originating from variance, bias, overfitting, and datasets coming from different distributions. Applied Deep Learning also discusses how to implement logistic regression completely from scratch without using any Python library except NumPy, to let you appreciate how libraries such as TensorFlow allow quick and efficient experiments. Case studies for each method are included to put into practice all theoretical information. You’ll discover tips and tricks for writing optimized Python code (for example vectorizing loops with NumPy). What You Will LearnImplement advanced techniques in the right way in Python and TensorFlowDebug and optimize advanced methods (such as dropout and regularization)Carry out error analysis (to realize if one has a bias problem, a variance problem, a data offset problem, and so on)Set up a machine learning project focused on deep learning on a complex datasetWho This Book Is ForReaders with a medium understanding of machine learning, linear algebra, calculus, and basic Python programming.
Applied Deep Learning and Computer Vision for Self-Driving Cars: Build autonomous vehicles using deep neural networks and behavior-cloning techniques
by Sumit Ranjan Dr. S. SenthamilarasuExplore self-driving car technology using deep learning and artificial intelligence techniques and libraries such as TensorFlow, Keras, and OpenCV Key Features Build and train powerful neural network models to build an autonomous car Implement computer vision, deep learning, and AI techniques to create automotive algorithms Overcome the challenges faced while automating different aspects of driving using modern Python libraries and architectures Book Description Thanks to a number of recent breakthroughs, self-driving car technology is now an emerging subject in the field of artificial intelligence and has shifted data scientists' focus to building autonomous cars that will transform the automotive industry. This book is a comprehensive guide to use deep learning and computer vision techniques to develop autonomous cars. Starting with the basics of self-driving cars (SDCs), this book will take you through the deep neural network techniques required to get up and running with building your autonomous vehicle. Once you are comfortable with the basics, you'll delve into advanced computer vision techniques and learn how to use deep learning methods to perform a variety of computer vision tasks such as finding lane lines, improving image classification, and so on. You will explore the basic structure and working of a semantic segmentation model and get to grips with detecting cars using semantic segmentation. The book also covers advanced applications such as behavior-cloning and vehicle detection using OpenCV, transfer learning, and deep learning methodologies to train SDCs to mimic human driving. By the end of this book, you'll have learned how to implement a variety of neural networks to develop your own autonomous vehicle using modern Python libraries. What you will learn Implement deep neural network from scratch using the Keras library Understand the importance of deep learning in self-driving cars Get to grips with feature extraction techniques in image processing using the OpenCV library Design a software pipeline that detects lane lines in videos Implement a convolutional neural network (CNN) image classifier for traffic signal signs Train and test neural networks for behavioral-cloning by driving a car in a virtual simulator Discover various state-of-the-art semantic segmentation and object detection architectures Who this book is for If you are a deep learning engineer, AI researcher, or anyone looking to implement deep learning and computer vision techniques to build self-driving blueprint solutions, this book is for you. Anyone who wants to learn how various automotive-related algorithms are built, will also find this book useful. Python programming experience, along with a basic understanding of deep learning, is necessary to get the most of this book.
Applied Deep Learning with Keras: Solve complex real-life problems with the simplicity of Keras
by Ritesh Bhagwat Mahla Abdolahnejad Matthew MoocarmeTake your neural networks to a whole new level with the simplicity and modularity of Keras, the most commonly used high-level neural networks API.Key FeaturesSolve complex machine learning problems with precisionEvaluate, tweak, and improve your deep learning models and solutionsUse different types of neural networks to solve real-world problemsBook DescriptionThough designing neural networks is a sought-after skill, it is not easy to master. With Keras, you can apply complex machine learning algorithms with minimum code.Applied Deep Learning with Keras starts by taking you through the basics of machine learning and Python all the way to gaining an in-depth understanding of applying Keras to develop efficient deep learning solutions. To help you grasp the difference between machine and deep learning, the book guides you on how to build a logistic regression model, first with scikit-learn and then with Keras. You will delve into Keras and its many models by creating prediction models for various real-world scenarios, such as disease prediction and customer churning. You’ll gain knowledge on how to evaluate, optimize, and improve your models to achieve maximum information. Next, you’ll learn to evaluate your model by cross-validating it using Keras Wrapper and scikit-learn. Following this, you’ll proceed to understand how to apply L1, L2, and dropout regularization techniques to improve the accuracy of your model. To help maintain accuracy, you’ll get to grips with applying techniques including null accuracy, precision, and AUC-ROC score techniques for fine tuning your model.By the end of this book, you will have the skills you need to use Keras when building high-level deep neural networks.What you will learnUnderstand the difference between single-layer and multi-layer neural network modelsUse Keras to build simple logistic regression models, deep neural networks, recurrent neural networks, and convolutional neural networksApply L1, L2, and dropout regularization to improve the accuracy of your modelImplement cross-validate using Keras wrappers with scikit-learnUnderstand the limitations of model accuracyWho this book is forIf you have basic knowledge of data science and machine learning and want to develop your skills and learn about artificial neural networks and deep learning, you will find this book useful. Prior experience of Python programming and experience with statistics and logistic regression will help you get the most out of this book. Although not necessary, some familiarity with the scikit-learn library will be an added bonus.
Applied Deep Learning with Python: Use scikit-learn, TensorFlow, and Keras to create intelligent systems and machine learning solutions
by Luis Capelo Alex GaleaA hands-on guide to deep learning that’s filled with intuitive explanations and engaging practical examplesKey FeaturesDesigned to iteratively develop the skills of Python users who don’t have a data science backgroundCovers the key foundational concepts you’ll need to know when building deep learning systemsFull of step-by-step exercises and activities to help build the skills that you need for the real-worldBook DescriptionTaking an approach that uses the latest developments in the Python ecosystem, you’ll first be guided through the Jupyter ecosystem, key visualization libraries and powerful data sanitization techniques before we train our first predictive model. We’ll explore a variety of approaches to classification like support vector networks, random decision forests and k-nearest neighbours to build out your understanding before we move into more complex territory. It’s okay if these terms seem overwhelming; we’ll show you how to put them to work.We’ll build upon our classification coverage by taking a quick look at ethical web scraping and interactive visualizations to help you professionally gather and present your analysis. It’s after this that we start building out our keystone deep learning application, one that aims to predict the future price of Bitcoin based on historical public data.By guiding you through a trained neural network, we’ll explore common deep learning network architectures (convolutional, recurrent, generative adversarial) and branch out into deep reinforcement learning before we dive into model optimization and evaluation. We’ll do all of this whilst working on a production-ready web application that combines Tensorflow and Keras to produce a meaningful user-friendly result, leaving you with all the skills you need to tackle and develop your own real-world deep learning projects confidently and effectively.What you will learnDiscover how you can assemble and clean your very own datasetsDevelop a tailored machine learning classification strategyBuild, train and enhance your own models to solve unique problemsWork with production-ready frameworks like Tensorflow and KerasExplain how neural networks operate in clear and simple termsUnderstand how to deploy your predictions to the webWho this book is forIf you're a Python programmer stepping into the world of data science, this is the ideal way to get started.
Applied Deep Learning with PyTorch: Demystify neural networks with PyTorch
by Hyatt SalehImplement techniques such as image classification and natural language processing (NLP) by understanding the different neural network architecturesKey FeaturesUnderstand deep learning and how it can solve complex real-world problemsApply deep learning for image classification and text processing using neural networks Develop deep learning solutions for tasks such as basic classification and solving style transfer problemsBook DescriptionMachine learning is rapidly becoming the most preferred way of solving data problems, thanks to the huge variety of mathematical algorithms that find patterns, which are otherwise invisible to us. Applied Deep Learning with PyTorch takes your understanding of deep learning, its algorithms, and its applications to a higher level. The book begins by helping you browse through the basics of deep learning and PyTorch. Once you are well versed with the PyTorch syntax and capable of building a single-layer neural network, you will gradually learn to tackle more complex data problems by configuring and training a convolutional neural network (CNN) to perform image classification. As you progress through the chapters, you’ll discover how you can solve an NLP problem by implementing a recurrent neural network (RNN). By the end of this book, you’ll be able to apply the skills and confidence you've gathered along your learning process to use PyTorch for building deep learning solutions that can solve your business data problems.What you will learnDetect a variety of data problems to which you can apply deep learning solutionsLearn the PyTorch syntax and build a single-layer neural network with itBuild a deep neural network to solve a classification problemDevelop a style transfer modelImplement data augmentation and retrain your modelBuild a system for text processing using a recurrent neural networkWho this book is forApplied Deep Learning with PyTorch is designed for data scientists, data analysts, and developers who want to work with data using deep learning techniques. Anyone looking to explore and implement advanced algorithms with PyTorch will also find this book useful. Some working knowledge of Python and familiarity with the basics of machine learning are a must. However, knowledge of NumPy and pandas will be beneficial, but not essential.
Applied Deep Learning with TensorFlow 2: Learn to Implement Advanced Deep Learning Techniques with Python
by Umberto MichelucciUnderstand how neural networks work and learn how to implement them using TensorFlow 2.0 and Keras. This new edition focuses on the fundamental concepts and at the same time on practical aspects of implementing neural networks and deep learning for your research projects.This book is designed so that you can focus on the parts you are interested in. You will explore topics as regularization, optimizers, optimization, metric analysis, and hyper-parameter tuning. In addition, you will learn the fundamentals ideas behind autoencoders and generative adversarial networks.All the code presented in the book will be available in the form of Jupyter notebooks which would allow you to try out all examples and extend them in interesting ways. A companion online book is available with the complete code for all examples discussed in the book and additional material more related to TensorFlow and Keras. All the code will be available in Jupyter notebook format and can be opened directly in Google Colab (no need to install anything locally) or downloaded on your own machine and tested locally.You will: • Understand the fundamental concepts of how neural networks work• Learn the fundamental ideas behind autoencoders and generative adversarial networks• Be able to try all the examples with complete code examples that you can expand for your own projects• Have available a complete online companion book with examples and tutorials.This book is for:Readers with an intermediate understanding of machine learning, linear algebra, calculus, and basic Python programming.
Applied Degree Education and the Future of Learning (Lecture Notes in Educational Technology)
by Christina Hong Will W. K. MaThis book draws on the responses to learning and teaching and applied education futures thinking, that provide insights into the future of learning. It brings together more than 30 novel and important applied research and scholarly contributions from around the world, including Australia, Canada, Finland, Germany, Hong Kong, Japan, Macau, Mainland China, Malaysia, Morocco, Pakistan, and the UK. The chapters, including reflective essays and practice-based case examples, are divided into five major themes:• Future ready values and competencies for the future of work• Innovative pedagogies in applied degree learning and training• Driving student access, engagement, and success through digital technologies• Intelligent technologies: Embedding the new world of work into applied degrees• Lifelong learning, partnering, and the future of workThis book is important for readers interested in international perspectives on the future of work and professional education.
Applied Degree Education and the Future of Work: Education 4.0 (Lecture Notes in Educational Technology)
by Christina Hong Will W. K. MaThis edited volume sets the stage for discussion on Education 4.0, with a focus on applied degree education and the future of work. Education 4.0 refers to the shifts in the education sector in response to Industry 4.0 where digital transformation is impacting the ways in which the world of work and our everyday lives are becoming increasingly automated. In the applied degree sector, significant change and transformation is occurring as leaders, educators and partners evolve smart campus environments to include blended learning, artificial intelligence, data analytics, BYOD devices, process automation and engage in curriculum renewal for and with industries and professions. This volume aims to profile and enhance the contribution of applied educational practice and research particularly in the applied degree sector and includes contributions that show case real world outcomes with students and industry as partners. This edited volume includes a wide range of topics, such as rethinking the role of education and educators; curriculum and the future of work; industrial partnership, collaboration and work integrated learning; vocational and professional practices; students, industry and professions as partners; employability skills and qualities for the 21st century world of work; innovative pedagogy and instructional design; adaptive learning technologies; and data analytics, assessment and feedback. The contributors come from different parts of the world in higher education, including, Canada, China, Finland, Germany, Hong Kong, Italy, Macau, Singapore and the United Kingdom.
Applied Degree Education and the Shape of Things to Come (Lecture Notes in Educational Technology)
by Christina Hong Will W. K. MaThis edited book seeks to evolve a global community of practice to share case studies, engage in critical discussion and spearhead thought leadership, to address the paradigm shift in next generation educational practice. This book showcases novel research studies in various forms and engenders interdisciplinary conversation and exchange concerning innovation, technology, and the role of applied education in workforce futures. It also equips readers with global perspectives on the latest developments in applied degree education and thinking on new education futures.
Applied Demography: An Introduction To Basic Concepts, Methods, And Data (Applied Demography Ser. #1)
by Steve H. MurdockThis text aims to help the novice understand demographic variables and analyze their impact on specific private and public sector interests. Examples are employed to demonstrate a wide range of techniques, and the book discusses software products from the 1990 US census that may revolutionize the use of demographic data by business and government.
Applied Demography and Public Health (Applied Demography Series #3)
by Benjamin S. Bradshaw Mary A. Mcgehee Nazrul HoqueThis book combines the disciplines of applied demography and public health by describing how applied demographic techniques can be used to help address public health issues. Besides addressing the impact of aging on health and health-related expenditure, cause-specific mortality, and maternal health and morbidity, the book provides several chapters on special analysis and methodological issues. The chapters provide a number of resources and tools that can be used in conducting research aimed at promoting public health. These resources include information on a variety of health research datasets, different statistical methodologies for analyzing health-related data and developing concepts related to health status, methodologies for forecasting or projecting disease incidences and associated costs, and discussions of demographic concepts used to measure population health status.
Applied Demography and Public Health in the 21st Century (Applied Demography Series #8)
by Mary A. Mcgehee M. Nazrul Hoque Beverly PecotteThis book demonstrates different statistical techniques for analyzing health-related data as well as providing new techniques for forecasting and/or projecting the incidence of diseases/disorders. It presents information on a variety of health related issues from the developed and developing world. Featuring cutting edge research from distinguished applied demographers and public health specialists, the book bridges the gap between theory and research. Each chapter provides methods and materials that can be used to conduct further research aimed at promoting public health issues. This book is intended for public health professionals, health policy makers, social epidemiologists, administrators, researchers, and students in the fields of applied demography and public health who are interested in exploring the potential of ground-breaking research or who want to further develop their existing research techniques. It complements another volume in the Applied Demography Series, Applied Demography and Public Health (Springer, 2013), which describes how applied demographic techniques can be used to help address public health issues.
Applied Dental Materials
by John F. Mccabe Angus WallsThis textbook covers all aspects of materials science relevant to the practice of dentistry. It is aimed primarily at undergraduate dental students, although it will also be useful for practising dentists, dental technicians and dental assistants.The 9th edition has been extensively revised to include the many advances in dental materials and their use that have occurred during the past nine years. The chapters on Resin-based filling materials and Adhesive restorative materials have been expanded significantly with new coverage of fibre reinforcement of composite structures and polymerisable luting agents. A brand new chapter has been added on endodontic materials.
Applied Depth of Field (Routledge Revivals)
by Alfred A. BlakerApplied Depth of Field (1985) is a complete and convenient guide for all kinds of photographers who need to exercise control over depth of field. This book assists photographers at all levels – from moderately experienced amateurs right up through working professionals – in learning how to control and to use depth of field, efficiently and quickly, for making all types of photo images.
Applied Design Research: A Mosaic of 22 Examples, Experiences and Interpretations Focussing on Bridging the Gap between Practice and Academics
by Peter JooreDesign and research are two fields of knowledge that each has its traditions, methods, standards and practices. These two worlds appear to be quite separate, with researchers investigating what exists, and designers visualising what could be. This book builds a bridge between both worlds by showing how design and research can be integrated to develop a new field of knowledge. Applied Design Research: A Mosaic of 22 Examples, Experiences and Interpretations Focussing on Bridging the Gap between Practice and Academics contains 22 inspiring reflections that demonstrate how the unique qualities of research (aimed at studying the present) and design (aimed at developing the future) can be combined. This book shows that the transdisciplinary approach is applicable in a multitude of sectors, ranging from healthcare, urban planning, circular economy, and the food industry. Arranged in five parts, the book offers a range of illustrative examples, experiences, methods, and interpretations. Together they make up the characteristic of a mosaic, each piece contributing a part of the complete picture, and all pieces together offering a multi-facted perspective of what applied design research is, how it is implemented and what the reader can expect from it. This book with its bearings in practice can enthuse early-stage researchers with the diversity of its examples, while more senior design researchers may recognize themselves in the depth of the experiences described and be inspired by them. The Open Access version of this book, available at www.taylorfrancis.com, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 license.
Applied Design Research in Living Labs and Other Experimental Learning and Innovation Environments
by Peter Joore Anja Overdiek Wina Smeenk Koen Van TurnhoutExperimental Learning and Innovation Environments, such as Living Labs, Field Labs, and Urban Innovation Labs, are increasingly used to connect multi-stakeholders in envisioning, creating, experimenting, learning, and trying out novel responses to diverse societal challenges. With designers facilitating the co-creation processes that take place in these labs, the design discipline plays an important role in these experimental environments.Applied Design Research in Living Labs and other Experimental Learning and Innovation Environments combines a focus on Experimental Learning and Innovation Environments (or Living Labs) with a focus on Applied Design Research. It offers an interdisciplinary perspective by bringing together diverse stakeholders from different disciplines. The book will adopt an interdisciplinary perspective, integrating insights from design, innovation, sociology, technology, and other relevant fields. It showcases real-world examples and case studies of successful Applied Design Research in Living Labs and focuses on design dilemmas that emerge while working in these Experimental Learning and Innovation Environments. The book explores the role of various stakeholders, including the roles that may play out during the development of Experimental Learning and Innovation Environments, and goes on to discuss the balance between fixed or fluid roles of these stakeholders and the polarity between working within one specific discipline versus working with various expertise or disciplines.Designers, government representatives, and researchers who apply a living lab approach to solve multi-stakeholder challenges in various fields by applying Urban Innovation Labs, Energy Living Labs, Mobility Living Labs, Health Living Labs, Education Living Labs, or Social Living Labs will find this book of interest.The Open Access version of this book, available at http://www.taylorfrancis.com, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives (CC-BY-NC-ND) 4.0 license.
Applied Differential Equations: The Primary Course (Textbooks in Mathematics #18)
by Vladimir A. DobrushkinThis book started as a collection of lecture notes for a course in differential equations taught by the Division of Applied Mathematics at Brown University. To some extent, it is a result of collective insights given by almost every instructor who taught such a course over the last 15 years. Therefore, the material and its presentation covered in this book were practically tested for many years. This text is designed for a two-semester sophomore or junior level course in differential equations. It offers novel approaches in presentation and utilization of computer capabilities. This text intends to provide a solid background in differential equations for students majoring in a breadth of fields. Differential equations are described in the context of applications. The author stresses differential equations constitute an essential part of modeling by showing their applications, including numerical algorithms and syntax of the four most popular software packages. Students learn how to formulate a mathematical model, how to solve differential equations (analytically or numerically), how to analyze them qualitatively, and how to interpret the results. In writing this textbook, the author aims to assist instructors and students through: •Showing a course in differential equations is essential for modeling real-life phenomena.•Stressing the mastery of traditional solution techniques and presenting effective methods, including reliable numerical approximations. •Providing qualitative analysis of ordinary differential equations. The reader should get an idea of how all solutions to the given problem behave, what are their validity intervals, whether there are oscillations, vertical or horizontal asymptotes, and what is their long-term behavior.•The reader will learn various methods of solving, analysis, visualization, and approximation, exploiting the capabilities of computers. •Introduces and employs Maple™, Mathematica®, MatLab®, and Maxima. •This textbook facilitates the development of the student’s skills to model real-world problems. Ordinary and partial differential equations is a classical subject that has been studied for about 300 years. The beauty and utility of differential equations and their application in mathematics, biology, chemistry, computer science, economics, engineering, geology, neuroscience, physics, the life sciences, and other fields reaffirm their inclusion in myriad curricula. A great number of examples and exercises make this text well suited for self-study or for traditional use by a lecturer in class. Therefore, this textbook addresses the needs of two levels of audience, the beginning and the advanced.
Applied Differential Equations with Boundary Value Problems (Textbooks in Mathematics)
by Vladimir DobrushkinApplied Differential Equations with Boundary Value Problems presents a contemporary treatment of ordinary differential equations (ODEs) and an introduction to partial differential equations (PDEs), including their applications in engineering and the sciences. This new edition of the author’s popular textbook adds coverage of boundary value problems. The text covers traditional material, along with novel approaches to mathematical modeling that harness the capabilities of numerical algorithms and popular computer software packages. It contains practical techniques for solving the equations as well as corresponding codes for numerical solvers. Many examples and exercises help students master effective solution techniques, including reliable numerical approximations. This book describes differential equations in the context of applications and presents the main techniques needed for modeling and systems analysis. It teaches students how to formulate a mathematical model, solve differential equations analytically and numerically, analyze them qualitatively, and interpret the results.
Applied Diffusion Processes from Engineering to Finance (Wiley-iste Ser.)
by Jacques Janssen Oronzio Manca Raimondo MancaThe aim of this book is to promote interaction between engineering, finance and insurance, as these three domains have many models and methods of solution in common for solving real-life problems. The authors point out the strict inter-relations that exist among the diffusion models used in engineering, finance and insurance. In each of the three fields, the basic diffusion models are presented and their strong similarities are discussed. Analytical, numerical and Monte Carlo simulation methods are explained with a view to applying them to obtain the solutions to the different problems presented in the book. Advanced topics such as nonlinear problems, Lévy processes and semi-Markov models in interactions with the diffusion models are discussed, as well as possible future interactions among engineering, finance and insurance. Contents 1. Diffusion Phenomena and Models.2. Probabilistic Models of Diffusion Processes.3. Solving Partial Differential Equations of Second Order.4. Problems in Finance.5. Basic PDE in Finance.6. Exotic and American Options Pricing Theory.7. Hitting Times for Diffusion Processes and Stochastic Models in Insurance.8. Numerical Methods.9. Advanced Topics in Engineering: Nonlinear Models.10. Lévy Processes.11. Advanced Topics in Insurance: Copula Models and VaR Techniques.12. Advanced Topics in Finance: Semi-Markov Models.13. Monte Carlo Semi-Markov Simulation Methods.
Applied Digital Signal Processing
by Dimitris G. Manolakis Vinay K. IngleManolakis and Ingle introduce the basic concepts and methodologies of DSP, leading students through the fundamental mathematical principles underlying signal processing. Students are shown how theory and practice connect as the authors: blend theory with practical implementation provide simple arguments and cases rather than detailed general proofs outline the limitations of digital signal processing teach through Matlab focus on algorithms that are of theoretical importance or useful in real-world applications Book jacket.