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Spatial Analysis for Radar Remote Sensing of Tropical Forests
This book is based on the authors’ extensive involvement in Synthetic Aperture Radar (SAR) mapping projects, targeting the health of an earth ecosystem with great relevance for climate change studies: the tropical forests. The subject is developed from a vantage point provided by analysis in a combined space, scale (frequency), time, wavelength, polarization domain. The combination of space and scale offers the capability to zoom in and out like a virtual microscope to the resolution in tune with the underlying ecological phenomenon. It also enables statistical measures (correlations) related to the forest spatial distribution in case of backscatter, or to the canopy height variations in case of interferometric observations. The time dimension brings into play measures of the ecosystem dynamics, such as the flooding extent in the swamp forests, deforestation or degradation events. The book’s spotlight is on radar spatial random fields, these being populated by either backscatter observations or elevation data from interferometric SAR. The basic tenet here is that the spatial statistic of the fields measured by the wavelet variance (in stationary or non-stationary situations) carries fingerprints of the forest structure.Features: Uniquely focused on specific techniques that provide multi-resolution spatial and temporal analysis of forest structure characteristics and changes. Examines several large and important international remote sensing projects aimed at documenting entire tropical ecosystems. Provides novel wavelet methods for tropical forest structural measures. Includes Python code for a suite of wavelet based time-series and single set InSAR coherence and backscatter speckle filters, available to download.As the first book on this topic, this composite approach appeals to both students learning through important case studies and to researchers finding new ideas for future studies.
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Applied Deep Learning with Python
A 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.
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Spatial Analysis for the Social Sciences
Many theories in the social sciences predict spatial dependence or the similarity of behaviors at neighboring locations. Spatial Analysis for the Social Sciences demonstrates how researchers can diagnose and model this spatial dependence and draw more valid inferences as a result. The book is structured around the well-known Galton's problem and presents a step-by-step guide to the application of spatial analysis. The book examines a variety of spatial diagnostics and models through a series of applied examples drawn from the social sciences. These include spatial lag models that capture behavioral diffusion between actors, spatial error models that account for spatial dependence in errors, and models that incorporate spatial heterogeneity in the effects of covariates. Spatial Analysis for the Social Sciences also examines advanced spatial models for time-series cross-sectional data, categorical and limited dependent variables, count data, and survival data.
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Bioimage Data Analysis Workflows
This Open Access textbook provides students and researchers in the life sciences with essential practical information on how to quantitatively analyze data images. It refrains from focusing on theory, and instead uses practical examples and step-by step protocols to familiarize readers with the most commonly used image processing and analysis platforms such as ImageJ, MatLab and Python. Besides gaining knowhow on algorithm usage, readers will learn how to create an analysis pipeline by scripting language; these skills are important in order to document reproducible image analysis workflows.The textbook is chiefly intended for advanced undergraduates in the life sciences and biomedicine without a theoretical background in data analysis, as well as for postdocs, staff scientists and faculty members who need to perform regular quantitative analyses of microscopy images.
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Symbolic Mathematics with Python
This book provides a hands-on approach to computer symbolic computation using the elementary commands of Python. Symbolic computer mathematics is the study of algorithms that can be implemented as computer programs which provide exact results rather than numerical approximations. The author begins by discussing Python essentials an number theory. Then, the book covers the simplification and evaluation of expressions involving multivariate rational functions, the exact solutions of systems of linear equations, and applications of polynomial algebra. Programs in symbolic differentiation and indefinite integration programs are also developed.
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Nonlinear Finite Element Analysis of Solids and Structures
Built upon the two original books by Mike Crisfield and their own lecture notes, renowned scientist René de Borst and his team offer a thoroughly updated yet condensed edition that retains and builds upon the excellent reputation and appeal amongst students and engineers alike for which Crisfield's first edition is acclaimed. Together with numerous additions and updates, the new authors have retained the core content of the original publication, while bringing an improved focus on new developments and ideas. This edition offers the latest insights in non-linear finite element technology, including non-linear solution strategies, computational plasticity, damage mechanics, time-dependent effects, hyperelasticity and large-strain elasto-plasticity. The authors' integrated and consistent style and unrivalled engineering approach assures this book's unique position within the computational mechanics literature. Key features: Combines the two previous volumes into one heavily revised text with obsolete material removed, an improved layout and updated references and notationsExtensive new material on more recent developments in computational mechanicsEasily readable, engineering oriented, with no more details in the main text than necessary to understand the concepts.Pseudo-code throughout makes the link between theory and algorithms, and the actual implementation.Accompanied by a website (www.wiley.com/go/deborst) with a Python code, based on the pseudo-code within the book and suitable for solving small-size problems. Non-linear Finite Element Analysis of Solids and Structures, 2nd Edition is an essential reference for practising engineers and researchers that can also be used as a text for undergraduate and graduate students within computational mechanics.
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Python for Beginners
Python is an amazing programming language. It can be applied to almost any programming task. It allows for rapid development and debugging. Getting started with Python is like learning any new skill: it’s important to find a resource you connect with to guide your learning. Luckily, there’s no shortage of excellent books that can help you learn both the basic concepts of programming and the specifics of programming in Python. With the abundance of resources, it can be difficult to identify which book would be best for your situation. Python for Beginners is a concise single point of reference for all material on python.Provides concise, need-to-know information on Python types and statements, special method names, built-in functions and exceptions, commonly used standard library modules, and other prominent Python toolsOffers practical advice for each major area of development with both Python 3.x and Python 2.xBased on the latest research in cognitive science and learning theoryHelps the reader learn how to write effective, idiomatic Python code by leveraging its best—and possibly most neglected—featuresThis book focuses on enthusiastic research aspirants who work on scripting languages for automating the modules and tools, development of web applications, handling big data, complex calculations, workflow creation, rapid prototyping, and other software development purposes. It also targets graduates, postgraduates in computer science, information technology, academicians, practitioners, and research scholars.
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Programming with Python
Based on the latest version of the language, this book offers a self-contained, concise and coherent introduction to programming with Python. The book’s primary focus is on realistic case study applications of Python. Each practical example is accompanied by a brief explanation of the problem-terminology and concepts, followed by necessary program development in Python using its constructs, and simulated testing. Given the open and participatory nature of development, Python has a variety of incorporated data structures, which has made it difficult to present it in a coherent manner. Further, some advanced concepts (super, yield, generator, decorator, etc.) are not easy to explain. The book specially addresses these challenges; starting with a minimal subset of the core, it offers users a step-by-step guide to achieving proficiency.
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Machine Learning for Emotion Analysis
Kickstart your emotion analysis journey with this hands-on, step-by-step guide to data science successKey Features* Discover the ins and outs of the end-to-end emotional analysis workflow* Explore the use of various ML models to derive meaningful insights from all sorts of data* Hone your craft by building and tweaking complex emotion analysis models in practical projectsBook DescriptionThe AI winter has long thawed, but many organizations are still failing to harness the power of machine learning (ML). If you want to tap that potential and add value to your own business with cutting-edge emotion analysis, you've found what you need in this trusty guide.In Machine Learning for Emotion Analysis, you'll take your foundational data science skills and grow them in the exciting realm of emotion analysis. With its practical approach, you'll be equipped with everything you need to give your company a clear insight into what your customers are thinking.This no-nonsense guide jumps right into the practicalities of emotion analysis, teaching you how to preprocess data, build a serviceable dataset, and ensure top-notch data quality. Once you're set up for success, we get hands-on with complex ML techniques. This is where you go from the intermediate to the advanced, covering deep neural networks, support vector machines, conditional probabilities, and more, as you experience the full breadth of possibilities with emotion analysis. The book finally rounds out with a couple of in-depth use cases – a sort of sandbox for you to experiment with your newly acquired skill set.By the end of this book, you'll be ready to present yourself as a valuable asset to any organization that takes data science seriously.What you will learn* Distinguish between sentiment analysis and emotion analysis* Master the art of data preprocessing and ensure high-quality input* Expand your use of data sources through data transformation* Build models that employ cutting-edge deep learning techniques* Discover how best to tune your models' hyperparameters* Explore the use of KNN, SVM, and DNNs for advanced use cases* Build APIs and integrate your models into existing solutions* Practice your new skills by working on real-world scenariosWho This Book Is ForThis book is for data scientists and Python developers who want to gain insights into what people are saying about their product, company, brand, governorship, and more. Basic knowledge of machine learning and Python programming knowledge is necessary to grasp the concepts covered.
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Statistics and Analysis of Scientific Data
This book is the third edition of a successful textbook for upper-undergraduate and early graduate students, which offers a solid foundation in probability theory and statistics and their application to physical sciences, engineering, biomedical sciences and related disciplines. It provides broad coverage ranging from conventional textbook content of probability theory, random variables, and their statistics, regression, and parameter estimation, to modern methods including Monte-Carlo Markov chains, resampling methods and low-count statistics.In addition to minor corrections and adjusting structure of the content, particular features in this new edition include:Python codes and machine-readable data for all examples, classic experiments, and exercises, which are now more accessible to students and instructorsNew chapters on low-count statistics including the Poisson-based Cash statistic for regression in the low-count regime, and on contingency tables and diagnostic testing.An additional example of classic experiments based on testing data for SARS-COV-2 to demonstrate practical applications of the described statistical methods. This edition inherits the main pedagogical method of earlier versions—a theory-then-application approach—where emphasis is placed first on a sound understanding of the underlying theory of a topic, which becomes the basis for an efficient and practical application of the materials. Basic calculus is used in some of the derivations, and no previous background in probability and statistics is required. The book includes many numerical tables of data as well as exercises and examples to aid the readers' understanding of the topic.
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