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Learning Professional Python Volume 2: Advanced

Learning Professional Python Volume 2: Advanced

Volume 2 of Learning Professional Python is a resource for students who want to learn Python even if they don’t have any programming knowledge and for teachers who want a comprehensive introduction to Python to use with their students. This book helps the students achieve their dream job in the IT Industry and teaches the students in an easy understandable manner while strengthening coding skills. Learning Professional Python: Volume 2 Objectives Become familiar with the features of Python programming language Introduce the object-oriented programming concepts Discover how to write Python code by following the object-oriented programming concepts Become comfortable with concepts such as classes objects inheritance dynamic dispatch interfaces and packages Learn the Python generics and collections Develop exception handling and the multithreaded applications Design graphical user interface (GUI) applications | Learning Professional Python Volume 2: Advanced

GBP 52.99
1

Mathematical Principles of the Internet Volume 2 Mathematics

Mathematical Principles of the Internet Volume 2 Mathematics

This two-volume set on Mathematical Principles of the Internet provides a comprehensive overview of the mathematical principles of Internet engineering. The books do not aim to provide all of the mathematical foundations upon which the Internet is based. Instead they cover a partial panorama and the key principles. Volume 1 explores Internet engineering while the supporting mathematics is covered in Volume 2. The chapters on mathematics complement those on the engineering episodes and an effort has been made to make this work succinct yet self-contained. Elements of information theory algebraic coding theory cryptography Internet traffic dynamics and control of Internet congestion and queueing theory are discussed. In addition stochastic networks graph-theoretic algorithms application of game theory to the Internet Internet economics data mining and knowledge discovery and quantum computation communication and cryptography are also discussed. In order to study the structure and function of the Internet only a basic knowledge of number theory abstract algebra matrices and determinants graph theory geometry analysis optimization theory probability theory and stochastic processes is required. These mathematical disciplines are defined and developed in the books to the extent that is needed to develop and justify their application to Internet engineering. | Mathematical Principles of the Internet Volume 2 Mathematics

GBP 44.99
1

Discovering Evolution Equations with Applications Volume 2-Stochastic Equations

Discovering Evolution Equations with Applications Volume 2-Stochastic Equations

Most existing books on evolution equations tend either to cover a particular class of equations in too much depth for beginners or focus on a very specific research direction. Thus the field can be daunting for newcomers to the field who need access to preliminary material and behind-the-scenes detail. Taking an applications-oriented conversational approach Discovering Evolution Equations with Applications: Volume 2-Stochastic Equations provides an introductory understanding of stochastic evolution equations. The text begins with hands-on introductions to the essentials of real and stochastic analysis. It then develops the theory for homogenous one-dimensional stochastic ordinary differential equations (ODEs) and extends the theory to systems of homogenous linear stochastic ODEs. The next several chapters focus on abstract homogenous linear nonhomogenous linear and semi-linear stochastic evolution equations. The author also addresses the case in which the forcing term is a functional before explaining Sobolev-type stochastic evolution equations. The last chapter discusses several topics of active research. Each chapter starts with examples of various models. The author points out the similarities of the models develops the theory involved and then revisits the examples to reinforce the theoretical ideas in a concrete setting. He incorporates a substantial collection of questions and exercises throughout the text and provides two layers of hints for selected exercises at the end of each chapter. Suitable for readers unfamiliar with analysis even at the undergraduate level this book offers an engaging and accessible account of core theoretical results of stochastic evolution equations in a way that gradually builds readers’ intuition. | Discovering Evolution Equations with Applications Volume 2-Stochastic Equations

GBP 69.99
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Linear Models and the Relevant Distributions and Matrix Algebra A Unified Approach Volume 2

Linear Models and the Relevant Distributions and Matrix Algebra A Unified Approach Volume 2

Linear Models and the Relevant Distributions and Matrix Algebra: A Unified Approach Volume 2 covers several important topics that were not included in the first volume. The second volume complements the first providing detailed solutions to the exercises in both volumes thereby greatly enhancing its appeal for use in advanced statistics programs. This volume can serve as a valuable reference. It can also serve as a resource in a mathematical statistics course for use in illustrating various theoretical concepts in the context of a relatively complex setting of great practical importance. Together with the first volume this volume provides a largely self-contained treatment of an important area of statistics and should prove highly useful to graduate students and others. Key Features: Includes solutions to the exercises from both the first and second volumes Includes coverage of several topics not covered in the first volume Highly valuable as a reference book for graduate students and researchers | Linear Models and the Relevant Distributions and Matrix Algebra A Unified Approach Volume 2

GBP 89.99
1

A Handbook of Statistical Analyses using SAS

A Handbook of Statistical Analyses using SAS

Updated to reflect SAS 9. 2 A Handbook of Statistical Analyses using SAS Third Edition continues to provide a straightforward description of how to conduct various statistical analyses using SAS. Each chapter shows how to use SAS for a particular type of analysis. The authors cover inference analysis of variance regression generalized linear models longitudinal data survival analysis principal components analysis factor analysis cluster analysis discriminant function analysis and correspondence analysis. They demonstrate the analyses through real-world examples including methadone maintenance treatment the relation of cirrhosis deaths to alcohol consumption a sociological study of children heart transplant treatment and crime rate determinants. With the data sets and SAS code available online this book remains the go-to resource for learning how to use SAS for many kinds of statistical analysis. It serves as a stepping stone to the wider resources available to SAS users.

GBP 180.00
1

Clinical Trial Optimization Using R

Clinical Trial Optimization Using R

Clinical Trial Optimization Using R explores a unified and broadly applicable framework for optimizing decision making and strategy selection in clinical development through a series of examples and case studies. It provides the clinical researcher with a powerful evaluation paradigm as well as supportive R tools to evaluate and select among simultaneous competing designs or analysis options. It is applicable broadly to statisticians and other quantitative clinical trialists who have an interest in optimizing clinical trials clinical trial programs or associated analytics and decision making. This book presents in depth the Clinical Scenario Evaluation (CSE) framework and discusses optimization strategies including the quantitative assessment of tradeoffs. A variety of common development challenges are evaluated as case studies and used to show how this framework both simplifies and optimizes strategy selection. Specific settings include optimizing adaptive designs multiplicity and subgroup analysis strategies and overall development decision-making criteria around Go/No-Go. After this book the reader will be equipped to extend the CSE framework to their particular development challenges as well.

GBP 44.99
1

Systems Medicine Physiological Circuits and the Dynamics of Disease

Systems Medicine Physiological Circuits and the Dynamics of Disease

Why do we get certain diseases whereas other diseases do not exist? In this book Alon one of the founders of systems biology builds a foundation for systems medicine. Starting from basic laws the book derives why physiological circuits are built the way they are. The circuits have fragilities that explain specific diseases and offer new strategies to treat them. By the end the reader will be able to use simple and powerful mathematical models to describe physiological circuits. The book explores in three parts hormone circuits immune circuits and aging and age-related disease. It culminates in a periodic table of diseases. Alon writes in a style accessible to a broad range of readers - undergraduates graduates or researchers from computational or biological backgrounds. The level of math is friendly and the math can even be bypassed altogether. For instructors and readers who want to go deeper the book includes dozens of exercises that have been rigorously tested in the classroom | Systems Medicine Physiological Circuits and the Dynamics of Disease

GBP 54.99
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Modeling and Simulation in Python

Modeling and Simulation in Python

The use of Python as a powerful computational tool is expanding with great strides. Python is a language which is easy to use and the libraries of tools provides it with efficient versatility. As the tools continue to expand users can create insightful models and simulations. While the tools offer an easy method to create a pipeline such constructions are not guaranteed to provide correct results. A lot of things can go wrong when building a simulation - deviously so. Users need to understand more than just how to build a process pipeline. Modeling and Simulation in Python introduces fundamental computational modeling techniques that are used in a variety of science and engineering disciplines. It emphasizes algorithmic thinking skills using different computational environments and includes a number of interesting examples including Shakespeare movie databases virus spread and Chess. Key Features: Several theories and applications are provided each with working Python scripts. All Python functions written for this book are archived on GitHub. Readers do not have to be Python experts but a working knowledge of the language is required. Students who want to know more about the foundations of modeling and simulation will find this an educational and foundational resource.

GBP 82.99
1

Line Integral Methods for Conservative Problems

Line Integral Methods for Conservative Problems

Line Integral Methods for Conservative Problems explains the numerical solution of differential equations within the framework of geometric integration a branch of numerical analysis that devises numerical methods able to reproduce (in the discrete solution) relevant geometric properties of the continuous vector field. The book focuses on a large set of differential systems named conservative problems particularly Hamiltonian systems. Assuming only basic knowledge of numerical quadrature and Runge–Kutta methods this self-contained book begins with an introduction to the line integral methods. It describes numerous Hamiltonian problems encountered in a variety of applications and presents theoretical results concerning the main instance of line integral methods: the energy-conserving Runge–Kutta methods also known as Hamiltonian boundary value methods (HBVMs). The authors go on to address the implementation of HBVMs in order to recover in the numerical solution what was expected from the theory. The book also covers the application of HBVMs to handle the numerical solution of Hamiltonian partial differential equations (PDEs) and explores extensions of the energy-conserving methods. With many examples of applications this book provides an accessible guide to the subject yet gives you enough details to allow concrete use of the methods. MATLAB codes for implementing the methods are available online.

GBP 59.99
1

Stochastic Processes An Introduction Third Edition

Stochastic Processes An Introduction Third Edition

Based on a well-established and popular course taught by the authors over many years Stochastic Processes: An Introduction Third Edition discusses the modelling and analysis of random experiments where processes evolve over time. The text begins with a review of relevant fundamental probability. It then covers gambling problems random walks and Markov chains. The authors go on to discuss random processes continuous in time including Poisson birth and death processes and general population models and present an extended discussion on the analysis of associated stationary processes in queues. The book also explores reliability and other random processes such as branching martingales and simple epidemics. A new chapter describing Brownian motion where the outcomes are continuously observed over continuous time is included. Further applications worked examples and problems and biographical details have been added to this edition. Much of the text has been reworked. The appendix contains key results in probability for reference. This concise updated book makes the material accessible highlighting simple applications and examples. A solutions manual with fully worked answers of all end-of-chapter problems and Mathematica® and R programs illustrating many processes discussed in the book can be downloaded from crcpress. com. | Stochastic Processes An Introduction Third Edition

GBP 46.99
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Pseudolinear Functions and Optimization

Pseudolinear Functions and Optimization

Pseudolinear Functions and Optimization is the first book to focus exclusively on pseudolinear functions a class of generalized convex functions. It discusses the properties characterizations and applications of pseudolinear functions in nonlinear optimization problems. The book describes the characterizations of solution sets of various optimization problems. It examines multiobjective pseudolinear multiobjective fractional pseudolinear static minmax pseudolinear and static minmax fractional pseudolinear optimization problems and their results. The authors extend these results to locally Lipschitz functions using Clarke subdifferentials. They also present optimality and duality results for h-pseudolinear and semi-infinite pseudolinear optimization problems. The authors go on to explore the relationships between vector variational inequalities and vector optimization problems involving pseudolinear functions. They present characterizations of solution sets of pseudolinear optimization problems on Riemannian manifolds as well as results on pseudolinearity of quadratic fractional functions. The book also extends n-pseudolinear functions to pseudolinear and n-pseudolinear fuzzy mappings and characterizations of solution sets of pseudolinear fuzzy optimization problems and n-pseudolinear fuzzy optimization problems. The text concludes with some applications of pseudolinear optimization problems to hospital management and economics. This book encompasses nearly all the published literature on the subject along with new results on semi-infinite nonlinear programming problems. It will be useful to readers from mathematical programming industrial engineering and operations management.

GBP 59.99
1

Mobile Data Visualization

Mobile Data Visualization

Mobile Data Visualization is about facilitating access to and understanding of data on mobile devices. Wearable trackers mobile phones and tablets are used by millions of people each day to read weather maps financial charts or personal health meters. What is required to create e­ffective visualizations for mobile devices? This book introduces key concepts of mobile data visualization and discusses opportunities and challenges from both research and practical perspectives. Mobile Data Visualization is the first book to provide an overview of how to e­ffectively visualize analyze and communicate data on mobile devices. Drawing from the expertise research and experience of an international range of academics and practitioners from across the domains of Visualization Human Computer Interaction and Ubiquitous Computing the book explores the challenges of mobile visualization and explains how it diff­ers from traditional data visualization. It highlights opportunities for reaching new audiences with engaging interactive and compelling mobile content. In nine chapters this book presents interesting perspectives on mobile data visualization including: how to characterize and classify mobile visualizations; how to interact with them while on the go and with limited attention spans; how to adapt them to various mobile contexts; specific methods on how to design and evaluate them; reflections on privacy ethical and other challenges as well as an outlook to a future of ubiquitous visualization. This accessible book is a valuable and rich resource for visualization designers practitioners researchers and students alike.

GBP 44.99
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Advanced Regression Models with SAS and R

Advanced Regression Models with SAS and R

Advanced Regression Models with SAS and R exposes the reader to the modern world of regression analysis. The material covered by this book consists of regression models that go beyond linear regression including models for right-skewed categorical and hierarchical observations. The book presents the theory as well as fully worked-out numerical examples with complete SAS and R codes for each regression. The emphasis is on model accuracy and the interpretation of results. For each regression the fitted model is presented along with interpretation of estimated regression coefficients and prediction of response for given values of predictors. Features: Presents the theoretical framework for each regression. Discusses data that are categorical count proportions right-skewed longitudinal and hierarchical. Uses examples based on real-life consulting projects. Provides complete SAS and R codes for each example. Includes several exercises for every regression. Advanced Regression Models with SAS and R is designed as a text for an upper division undergraduate or a graduate course in regression analysis. Prior exposure to the two software packages is desired but not required. The Author:Olga Korosteleva is a Professor of Statistics at California State University Long Beach. She teaches a large variety of statistical courses to undergraduate and master’s students. She has published three statistical textbooks. For a number of years she has held the position of faculty director of the statistical consulting group. Her research interests lie mostly in applications of statistical methodology through collaboration with her clients in health sciences nursing kinesiology and other fields.

GBP 44.99
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Foundations of Predictive Analytics

Foundations of Predictive Analytics

Drawing on the authors’ two decades of experience in applied modeling and data mining Foundations of Predictive Analytics presents the fundamental background required for analyzing data and building models for many practical applications such as consumer behavior modeling risk and marketing analytics and other areas. It also discusses a variety of practical topics that are frequently missing from similar texts. The book begins with the statistical and linear algebra/matrix foundation of modeling methods from distributions to cumulant and copula functions to Cornish–Fisher expansion and other useful but hard-to-find statistical techniques. It then describes common and unusual linear methods as well as popular nonlinear modeling approaches including additive models trees support vector machine fuzzy systems clustering naïve Bayes and neural nets. The authors go on to cover methodologies used in time series and forecasting such as ARIMA GARCH and survival analysis. They also present a range of optimization techniques and explore several special topics such as Dempster–Shafer theory. An in-depth collection of the most important fundamental material on predictive analytics this self-contained book provides the necessary information for understanding various techniques for exploratory data analysis and modeling. It explains the algorithmic details behind each technique (including underlying assumptions and mathematical formulations) and shows how to prepare and encode data select variables use model goodness measures normalize odds and perform reject inference. Web ResourceThe book’s website at www. DataMinerXL. com offers the DataMinerXL software for building predictive models. The site also includes more examples and information on modeling.

GBP 59.99
1

Banach Limit and Applications

Banach Limit and Applications

Banach Limit and Applications provides all the results in the area of Banach Limit its extensions generalizations and applications to various fields in one go (as far as possible). All the results in this field after Banach introduced this concept in 1932 were scattered till now. Sublinear functionals generating and dominating Banach Limit unique Banach Limit (almost convergence) invariant means and invariant limits absolute and strong almost convergence applications to ergodicity law of large numbers Fourier series uniform distribution of sequences uniform density core theorems and functional Banach limits are discussed in this book. The discovery of functional analysis such as the Hahn-Banach Theorem and the Banach-Steinhaus Theorem helped the researchers to develop a modern rich and unified theory of sequence spaces by encompassing classical summability theory via matrix transformations and the topics related to sequence spaces which arose from the concept of Banach limits all of which are presented in this book. The unique features of this book are as follows: All the results in this area which were scattered till now are in one place. The book is the first of its kind in the sense that there is no other competitive book. The contents of this monograph did not appear in any book form before. The audience of this book are the researchers in this area and Ph. D. and advanced master’s students. The book is suitable for one- or two-semester course work for Ph. D. students M. S. students in North America and Europe and M. Phil. and master’s students in India.

GBP 130.00
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Artificial Superintelligence A Futuristic Approach

Artificial Superintelligence A Futuristic Approach

A day does not go by without a news article reporting some amazing breakthrough in artificial intelligence (AI). Many philosophers futurists and AI researchers have conjectured that human-level AI will be developed in the next 20 to 200 years. If these predictions are correct it raises new and sinister issues related to our future in the age of intelligent machines. Artificial Superintelligence: A Futuristic Approach directly addresses these issues and consolidates research aimed at making sure that emerging superintelligence is beneficial to humanity. While specific predictions regarding the consequences of superintelligent AI vary from potential economic hardship to the complete extinction of humankind many researchers agree that the issue is of utmost importance and needs to be seriously addressed. Artificial Superintelligence: A Futuristic Approach discusses key topics such as: AI-Completeness theory and how it can be used to see if an artificial intelligent agent has attained human level intelligence Methods for safeguarding the invention of a superintelligent system that could theoretically be worth trillions of dollars Self-improving AI systems: definition types and limits The science of AI safety engineering including machine ethics and robot rights Solutions for ensuring safe and secure confinement of superintelligent systems The future of superintelligence and why long-term prospects for humanity to remain as the dominant species on Earth are not great Artificial Superintelligence: A Futuristic Approach is designed to become a foundational text for the new science of AI safety engineering. AI researchers and students computer security researchers futurists and philosophers should find this an invaluable resource. | Artificial Superintelligence A Futuristic Approach

GBP 180.00
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Real Analysis and Foundations

Real Analysis and Foundations

Through four editions this popular textbook attracted a loyal readership and widespread use. Students find the book to be concise accessible and complete. Instructors find the book to be clear authoritative and dependable. The primary goal of this new edition remains the same as in previous editions. It is to make real analysis relevant and accessible to a broad audience of students with diverse backgrounds while also maintaining the integrity of the course. This text aims to be the generational touchstone for the subject and the go-to text for developing young scientists. This new edition continues the effort to make the book accessible to a broader audience. Many students who take a real analysis course do not have the ideal background. The new edition offers chapters on background material like set theory logic and methods of proof. The more advanced material in the book is made more apparent. This new edition offers a new chapter on metric spaces and their applications. Metric spaces are important in many parts of the mathematical sciences including data mining web searching and classification of images. The author also revised the material on sequences and series adding examples and exercises that compare convergence tests and give additional tests. The text includes rare topics such as wavelets and applications to differential equations. The level of difficulty moves slowly becoming more sophisticated in later chapters. Students have commented on the progression as a favorite aspect of the textbook. The author is perhaps the most prolific expositor of upper division mathematics. With over seventy books in print thousands of students have been taught and learned from his books. | Real Analysis and Foundations

GBP 82.99
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Modeling and Inverse Problems in the Presence of Uncertainty

Modeling and Inverse Problems in the Presence of Uncertainty

Modeling and Inverse Problems in the Presence of Uncertainty collects recent research—including the authors’ own substantial projects—on uncertainty propagation and quantification. It covers two sources of uncertainty: where uncertainty is present primarily due to measurement errors and where uncertainty is present due to the modeling formulation itself. After a useful review of relevant probability and statistical concepts the book summarizes mathematical and statistical aspects of inverse problem methodology including ordinary weighted and generalized least-squares formulations. It then discusses asymptotic theories bootstrapping and issues related to the evaluation of correctness of assumed form of statistical models. The authors go on to present methods for evaluating and comparing the validity of appropriateness of a collection of models for describing a given data set including statistically based model selection and comparison techniques. They also explore recent results on the estimation of probability distributions when they are embedded in complex mathematical models and only aggregate (not individual) data are available. In addition they briefly discuss the optimal design of experiments in support of inverse problems for given models. The book concludes with a focus on uncertainty in model formulation itself covering the general relationship of differential equations driven by white noise and the ones driven by colored noise in terms of their resulting probability density functions. It also deals with questions related to the appropriateness of discrete versus continuum models in transitions from small to large numbers of individuals. With many examples throughout addressing problems in physics biology and other areas this book is intended for applied mathematicians interested in deterministic and/or stochastic models and their interactions. It is also s

GBP 59.99
1

Business Financial Planning with Microsoft Excel

Business Financial Planning with Microsoft Excel

Business Finance Planning with Microsoft® Excel® shows how to visualize plan and put into motion an idea for creating a start-up company. Microsoft Excel is a tool that makes it easier to build a business financial planning process for a new business venture. With an easy-to follow structure the book flows as a six-step process: Presenting a case study of a business start-up Creating goals and objectives Determining expenses from those goals and objectives Estimating potential sales revenue based on what competitors charge their customers Predicting marketing costs Finalizing the financial analysis with a of financial statements. Written around an IT startup case study the book presents a host of Excel worksheets describing the case study along with accompanying blank forms. Readers can use these forms in their own businesses so they can build parts of their own business plans as they go. This is intended to be a practical guide that teaches and demonstrates by example in the end presenting a usable financial model to build and tweak a financial plan with a set of customizable Excel worksheets. The book uses practical techniques to help with the planning processing. These include applying a SWOT (strengths weaknesses opportunities and threats) matrix to evaluate a business idea and SMART (Specific Measurable Achievable Relevant and Time-Bound) objectives to link together goals. As the book concludes readers will be able to develop their own income statement balance sheet and the cash-flow statement for a full analysis of their new business ideas. Worksheets are available to download from: https://oracletroubleshooter. com/business-finance-planning/app/ | Business Financial Planning with Microsoft Excel

GBP 34.99
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Python for Scientific Computing and Artificial Intelligence

Python for Scientific Computing and Artificial Intelligence

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

GBP 52.99
1

Telling Stories with Data With Applications in R

Telling Stories with Data With Applications in R

The book equips students with the end-to-end skills needed to do data science. That means gathering cleaning preparing and sharing data then using statistical models to analyse data writing about the results of those models drawing conclusions from them and finally using the cloud to put a model into production all done in a reproducible way. At the moment there are a lot of books that teach data science but most of them assume that you already have the data. This book fills that gap by detailing how to go about gathering datasets cleaning and preparing them before analysing them. There are also a lot of books that teach statistical modelling but few of them teach how to communicate the results of the models and how they help us learn about the world. Very few data science textbooks cover ethics and most of those that do have a token ethics chapter. Finally reproducibility is not often emphasised in data science books. This book is based around a straight-forward workflow conducted in an ethical and reproducible way: gather data prepare data analyse data and communicate those findings. This book will achieve the goals by working through extensive case studies in terms of gathering and preparing data and integrating ethics throughout. It is specifically designed around teaching how to write about the data and models so aspects such as writing are explicitly covered. And finally the use of GitHub and the open-source statistical language R are built in throughout the book. Key Features: Extensive code examples. Ethics integrated throughout. Reproducibility integrated throughout. Focus on data gathering messy data and cleaning data. Extensive formative assessment throughout. | Telling Stories with Data With Applications in R

GBP 74.99
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Foundations of Reinforcement Learning with Applications in Finance

Foundations of Reinforcement Learning with Applications in Finance

Foundations of Reinforcement Learning with Applications in Finance aims to demystify Reinforcement Learning and to make it a practically useful tool for those studying and working in applied areas — especially finance. Reinforcement Learning is emerging as a powerful technique for solving a variety of complex problems across industries that involve Sequential Optimal Decisioning under Uncertainty. Its penetration in high-profile problems like self-driving cars robotics and strategy games points to a future where Reinforcement Learning algorithms will have decisioning abilities far superior to humans. But when it comes getting educated in this area there seems to be a reluctance to jump right in because Reinforcement Learning appears to have acquired a reputation for being mysterious and technically challenging. This book strives to impart a lucid and insightful understanding of the topic by emphasizing the foundational mathematics and implementing models and algorithms in well-designed Python code along with robust coverage of several financial trading problems that can be solved with Reinforcement Learning. This book has been created after years of iterative experimentation on the pedagogy of these topics while being taught to university students as well as industry practitioners. Features Focus on the foundational theory underpinning Reinforcement Learning and software design of the corresponding models and algorithms Suitable as a primary text for courses in Reinforcement Learning but also as supplementary reading for applied/financial mathematics programming and other related courses Suitable for a professional audience of quantitative analysts or data scientists Blends theory/mathematics programming/algorithms and real-world financial nuances while always striving to maintain simplicity and to build intuitive understanding To access the code base for this book please go to: https://github. com/TikhonJelvis/RL-book

GBP 74.99
1

Engineering Production-Grade Shiny Apps

Engineering Production-Grade Shiny Apps

From the Reviews [This book] contains an excellent blend of both Shiny-specific topics … and practical advice from software development that fits in nicely with Shiny apps. You will find many nuggets of wisdom sprinkled throughout these chapters…. Eric Nantz Host of the R-Podcast and the Shiny Developer Series (from the Foreword) [This] book is a gradual and pleasant invitation to the production-ready shiny apps world. It …exposes a comprehensive and robust workflow powered by the {golem} package. [It] fills the not yet covered gap between shiny app development and deployment in such a thrilling way that it may be read in one sitting…. In the industry world where processes robustness is a key toward productivity this book will indubitably have a tremendous impact. David Granjon Sr. Expert Data Science Novartis Presented in full color Engineering Production-Grade Shiny Apps helps people build production-grade shiny applications by providing advice tools and a methodology to work on web applications with R. This book starts with an overview of the challenges which arise from any big web application project: organizing work thinking about the user interface the challenges of teamwork and the production environment. Then it moves to a step-by-step methodology that goes from the idea to the end application. Each part of this process will cover in detail a series of tools and methods to use while building production-ready shiny applications. Finally the book will end with a series of approaches and advice about optimizations for production. Features Focused on practical matters: This book does not cover Shiny concepts but practical tools and methodologies to use for production. Based on experience: This book is a formalization of several years of experience building Shiny applications. Original content: This book presents new methodologies and tooling not just a review of what already exists. Engineering Production-Grade Shiny Apps covers medium to advanced content about Shiny so it will help people that are already familiar with building apps with Shiny and who want to go one step further.

GBP 48.99
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Tidy Finance with R

Tidy Finance with R

This textbook shows how to bring theoretical concepts from finance and econometrics to the data. Focusing on coding and data analysis with R we show how to conduct research in empirical finance from scratch. We start by introducing the concepts of tidy data and coding principles using the tidyverse family of R packages. Code is provided to prepare common open-source and proprietary financial data sources (CRSP Compustat Mergent FISD TRACE) and organize them in a database. We reuse these data in all the subsequent chapters which we keep as self-contained as possible. The empirical applications range from key concepts of empirical asset pricing (beta estimation portfolio sorts performance analysis Fama-French factors) to modeling and machine learning applications (fixed effects estimation clustering standard errors difference-in-difference estimators ridge regression Lasso Elastic net random forests neural networks) and portfolio optimization techniques. Highlights 1. Self-contained chapters on the most important applications and methodologies in finance which can easily be used for the reader’s research or as a reference for courses on empirical finance. 2. Each chapter is reproducible in the sense that the reader can replicate every single figure table or number by simply copying and pasting the code we provide. 3. A full-fledged introduction to machine learning with tidymodels based on tidy principles to show how factor selection and option pricing can benefit from Machine Learning methods. 4. Chapter 2 on accessing and managing financial data shows how to retrieve and prepare the most important datasets financial economics: CRSP and Compustat. The chapter also contains detailed explanations of the most relevant data characteristics. 5. Each chapter provides exercises based on established lectures and classes which are designed to help students to dig deeper. The exercises can be used for self-studying or as a source of inspiration for teaching exercises. | Tidy Finance with R

GBP 59.99
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Teaching Mathematics at a Technical College

Teaching Mathematics at a Technical College

Not much has been written about technical colleges especially teaching mathematics at one. Much had been written about community college mathematics. This book addresses this disparity. Mathematics is a beautiful subject worthy to be taught at the technical college level. The author sheds light on technical colleges and their importance in the higher education system. Technical colleges area more affordable for students and provide many career opportunities. These careers are becoming or have become as lucrative as careers requiring a four-year-degree. The interest in technical college education is likely to continue to grow. Mathematics like all other classes is a subject that needs time energy and dedication to learn. For an instructor it takes many years of hard work and dedication just to be able to teach the subject. Students should not be expected to learn the mathematics overnight. As instructors we need to be open honest and put forth our very best to our students so that they can see that they are able to succeed in whatever is placed in front of them. This book hopes to encourage such an effort. A notable percentage of students who are receiving associate degrees will go through at least one of more mathematics courses. These students should not be forgotten about—their needs are similar to any student who is required to take a mathematics course to earn a degree. This book offers insight into teaching mathematics at a technical college. It is also a source for students to turn toward when they are feeling dread in taking a mathematics course. Mathematics instructors want to help students succeed. If they put forth their best effort and us ours we can all work as one team to get the student through the course and onto chasing their dreams. Though this book focuses on teaching mathematics some chapters expand to focus on teaching in general. The overall hope is the reader will be inspired by the great work that is happening at technical colleges all around the country. Technical college can be should be and is the backbone of the American working class.

GBP 22.99
1