The.Hottest

Wednesday, July 01, 2020 , ,
Rick Durrett ... 430 pages - Publisher: Cambridge Univ. Press; 5th edition (May, 2019) ... Language: English - ISBN-10: 1108473687 - ISBN-13: 978-1108473682.

This lively introduction to measure-theoretic probability theory covers laws of large numbers, central limit theorems, random walks, martingales, Markov chains, ergodic theorems, and Brownian motion. Concentrating on results that are the most useful for applications, this comprehensive treatment is a rigorous graduate text and reference. Operating under the philosophy that the best way to learn probability is to see it in action, the book contains extended examples that apply the theory to concrete applications. This fifth edition contains a new chapter on multidimensional Brownian motion and its relationship to partial differential equations (PDEs), an advanced topic that is finding new applications. Setting the foundation for this expansion, Chapter 7 now features a proof of Itô's formula. Key exercises that previously were simply proofs left to the reader have been directly inserted into the text as lemmas. The new edition re-instates discussion about the central limit theorem for martingales and stationary sequences.
Read

Tuesday, June 30, 2020 , , ,
Nilanjan Dey, Sanjeev Wagh, Parikshit N. Mahalle, Mohd. Shafi Pathan ... 243 pages - Publisher: CRC Press; (May, 2019) ... Language: English - AmazonSIN: B07S4BVPSL.

The book focuses on how machine learning and the Internet of Things (IoT) has empowered the advancement of information driven arrangements including key concepts and advancements. Ontologies that are used in heterogeneous IoT environments have been discussed including interpretation, context awareness, analyzing various data sources, machine learning algorithms and intelligent services and applications. Further, it includes unsupervised and semi-supervised machine learning techniques with study of semantic analysis and thorough analysis of reviews. Divided into sections such as machine learning, security, IoT and data mining, the concepts are explained with practical implementation including results.

Key Features: Follows an algorithmic approach for data analysis in machine learning + Introduces machine learning methods in applications + Address the emerging issues in computing such as deep learning, machine learning, Internet of Things and data analytics + Focuses on machine learning techniques namely unsupervised and semi-supervised for unseen and seen data sets + Case studies are covered relating to human health, transportation and Internet applications
Read

Monday, June 29, 2020 , ,
Mykel J. Kochenderfer, Tim A. Wheeler ... 520 pages - Publisher: The MIT Press; (March, 2019) ... Language: English - ISBN-10: 0262039427 - ISBN-13: 978-0262039420.

A comprehensive introduction to optimization with a focus on practical algorithms for the design of engineering systems: This book offers a comprehensive introduction to optimization with a focus on practical algorithms. The book approaches optimization from an engineering perspective, where the objective is to design a system that optimizes a set of metrics subject to constraints. Readers will learn about computational approaches for a range of challenges, including searching high-dimensional spaces, handling problems where there are multiple competing objectives, and accommodating uncertainty in the metrics. Figures, examples, and exercises convey the intuition behind the mathematical approaches. The text provides concrete implementations in the Julia programming language. Topics covered include derivatives and their generalization to multiple dimensions; local descent and first- and second-order methods that inform local descent; stochastic methods, which introduce randomness into the optimization process; linear constrained optimization, when both the objective function and the constraints are linear; surrogate models, probabilistic surrogate models, and using probabilistic surrogate models to guide optimization; optimization under uncertainty; uncertainty propagation; expression optimization; and multidisciplinary design optimization. Appendixes offer an introduction to the Julia language, test functions for evaluating algorithm performance, and mathematical concepts used in the derivation and analysis of the optimization methods discussed in the text. The book can be used by advanced undergraduates and graduate students in mathematics, statistics, computer science, any engineering field, (including electrical engineering and aerospace engineering), and operations research, and as a reference for professionals.
Read

Sunday, June 28, 2020 , ,
Robert Yori, Marcus Kim, Lance Kirby ... 1104 pages - Publisher: Sybex; (December, 2019) ... Language: English - ISBN-10: 1119570123 - ISBN-13: 978-1119570127.

The best-selling Revit guide, now more complete than ever with all-new coverage on the 2020 release: Mastering Autodesk Revit 2020 is packed with focused discussions, detailed exercises, and real-world examples to help you get up to speed quickly on the latest version of Autodesk Revit. Organized according to how you learn and implement the software, this book provides expert guidance for all skill levels. Hands-on tutorials allow you to dive right in and start accomplishing vital tasks, while compelling examples illustrate how Revit for Architecture is used in every project. Available online downloads include before-and-after tutorial files and additional advanced content to help you quickly master this powerful software. From basic interface topics to advanced visualization techniques and documentation, this invaluable guide is your ideal companion through the Revit workflow. Whether you're preparing for Autodesk certification exams or just want to become more productive with the architectural design software, practical exercises and expert instruction will get you where you need to be. Understand key BIM and Revit concepts and master the Revit interface. Delve into templates, work-sharing, and managing Revit projects. Master modeling and massing, the Family Editor, and visualization techniques. Explore documentation, including annotation, detailing, and complex structures. BIM software has become a mandatory asset in today's architecture field; automated documentation updates reduce errors while saving time and money, and Autodesk's Revit is the industry leader in the BIM software space.
Read

Saturday, June 27, 2020 , ,
Michael Law, Amy Collins ... 450 pages - Publisher: Esri Press; (March, 2016) ... Language: English - ASIN: B01D0RBGMK.

In the tradition of the best-selling Getting to Know series, Getting to Know ArcGIS Pro teaches new and existing GIS users how to get started solving problems by visualizing, querying, creating, editing, analyzing, and presenting geospatial data in both 2D and 3D environments using the latest ArcGIS mapping app, ArcGIS Pro. This book teaches the basic functions and capabilities of ArcGIS Pro through practical project workflows and shows how it is an essential component of the ArcGIS platform. Data for completing the exercises and trial software are available for download (esri.com/esripress-resources). A supplement based on ArcGIS Pro 2.2 to help you complete exercises in the book is also available on the book resources page.
Read

Friday, June 26, 2020 , ,
Feng Fu ... 302 pages - Publisher: Butterworth-Heinemann; (February, 2018) ... Language: English - AmazonSIN: B079KCFLPP.

The design of tall buildings and complex structures involves challenging activities, including: scheme design, modelling, structural analysis and detailed design. This book provides structural designers with a systematic approach to anticipate and solve issues for tall buildings and complex structures. This book begins with a clear and rigorous exposition of theories behind designing tall buildings. After this is an explanation of basic issues encountered in the design process. This is followed by chapters concerning the design and analysis of tall building with different lateral stability systems, such as MRF, shear wall, core, outrigger, bracing, tube system, diagrid system and mega frame. The final three chapters explain the design principles and analysis methods for complex and special structures. With this book, researchers and designers will find a valuable reference on topics such as tall building systems, structure with complex geometry, Tensegrity structures, membrane structures and offshore structures.

Features: Numerous worked-through examples of existing prestigious projects around the world (such as Jeddah Tower, Shanghai Tower, and Petronas Tower etc.) are provided to assist the reader’s understanding of the topic + Provides the latest modelling methods in design such as BIM and Parametric Modelling technique + Detailed explanations of widely used programs in current design practice, such as SAP2000, ETABS, ANSYS, and Rhino + Modelling case studies for all types of tall buildings and complex structures, such as: Buttressed Core system, diagrid system, Tube system, Tensile structures and offshore structures etc.
Read

Thursday, June 25, 2020 , ,
Benjamin Bengfort, Rebecca Bilbro, Tony Ojeda ... 332 pages - Publisher: O'Reilly Media; (July, 2018) ... Language: English - ISBN-10: 1491963042 - ISBN-13: 978-1491963043.

From news and speeches to informal chatter on social media, natural language is one of the richest and most underutilized sources of data. Not only does it come in a constant stream, always changing and adapting in context; it also contains information that is not conveyed by traditional data sources. The key to unlocking natural language is through the creative application of text analytics. This practical book presents a data scientist’s approach to building language-aware products with applied machine learning. You’ll learn robust, repeatable, and scalable techniques for text analysis with Python, including contextual and linguistic feature engineering, vectorization, classification, topic modeling, entity resolution, graph analysis, and visual steering. By the end of the book, you’ll be equipped with practical methods to solve any number of complex real-world problems.

Preprocess and vectorize text into high-dimensional feature representations + Perform document classification and topic modeling + Steer the model selection process with visual diagnostics + Extract key phrases, named entities, and graph structures to reason about data in text + Build a dialog framework to enable chatbots and language-driven interaction + Use Spark to scale processing power and neural networks to scale model complexity.
Read

Wednesday, June 24, 2020 , ,
Yasser Shoukry, Jaiprakash Pandey ... 826 pages - Publisher: Packt Publishing; (May, 2020) ... Language: English - ISBN-10: 1789809150 - ISBN-13: 978-1789809152.

AutoCAD and AutoCAD LT are one of the most versatile software applications for architectural and engineering designs and the most popular computer-aided design (CAD) platform for 2D drafting and 3D modeling. This hands-on guide will take you through everything you need to know to make the most out of this powerful tool, starting from a simple tour of the user interface through to using advanced tools. Starting with basic drawing shapes and functions, you'll get to grips with the fundamentals of CAD designs. You'll then learn about effective drawing management using layers, dynamic blocks, and groups and discover how to add annotations and plot like professionals. The book delves into 3D modeling and helps you convert your 2D drawings into 3D models and shapes. As you progress, you'll cover advanced tools and features such as isometric drawings, drawing utilities for managing and recovering complex files, quantity surveying, and multidisciplinary drawing files using xRefs, and you'll learn how to implement them with the help of practical exercises at the end of each chapter. Finally, you'll get to grips with rendering and visualizing your designs in AutoCAD. By the end of the book, you'll have developed a solid understanding of CAD principles and be able to work with AutoCAD software confidently to build impressive 2D and 3D drawings.
Read

Tuesday, June 23, 2020 , , ,
Ryan Mitchell ... 308 pages - Publisher: O'Reilly Media; 2nd edition (April, 2018) ... Language: English - ISBN-10: 1491985577 - ISBN-13: 978-1491985571.

If programming is magic then web scraping is surely a form of wizardry. By writing a simple automated program, you can query web servers, request data, and parse it to extract the information you need. The expanded edition of this practical book not only introduces you web scraping, but also serves as a comprehensive guide to scraping almost every type of data from the modern web. Part I focuses on web scraping mechanics: using Python to request information from a web server, performing basic handling of the server’s response, and interacting with sites in an automated fashion. Part II explores a variety of more specific tools and applications to fit any web scraping scenario you’re likely to encounter. Parse complicated HTML pages + Develop crawlers with the Scrapy framework + Learn methods to store data you scrape + Read and extract data from documents + Clean and normalize badly formatted data + Read and write natural languages + Crawl through forms and logins + Scrape JavaScript and crawl through APIs + Use and write image-to-text software + Avoid scraping traps and bot blockers + Use scrapers to test your website.
Read

Monday, June 22, 2020 ,
Robert V. Hogg, Joseph W. McKean, Allen T. Craig ... 768 pages - Publisher: Pearson; 8th edition (January, 2018) ... Language: English - ISBN-10: 0134686993 - ISBN-13: 978-0134686998.

Comprehensive coverage of mathematical statistics — with a proven approach: Introduction to Mathematical Statistics by Hogg, McKean, and Craig enhances student comprehension and retention with numerous, illustrative examples and exercises. Classical statistical inference procedures in estimation and testing are explored extensively, and the text’s flexible organization makes it ideal for a range of mathematical statistics courses. Substantial changes to the 8th Edition — many based on user feedback — help students appreciate the connection between statistical theory and statistical practice, while other changes enhance the development and discussion of the statistical theory presented.
Read

Sunday, June 21, 2020 , , ,
Prasad Kothari ... 176 pages - Publisher: Packt Publishing; (October, 2015) ... Language: English - AmazonSIN: B013R02BOC.

STATA is an integrated software package that provides you with everything you need for data analysis, data management, and graphics. STATA also provides you with a platform to efficiently perform simulation, regression analysis (linear and multiple) [and custom programming. This book covers data management, graphs visualization, and programming in STATA. Starting with an introduction to STATA and data analytics you'll move on to STATA programming and data management. Next, the book takes you through data visualization and all the important statistical tests in STATA. Linear and logistic regression in STATA is also covered. As you progress through the book, you will explore a few analyses, including the survey analysis, time series analysis, and survival analysis in STATA. You'll also discover different types of statistical modelling techniques and learn how to implement these techniques in STATA.
Read

Saturday, June 20, 2020 , ,
David R. H. Jones, Mike F. Ashby ... 586 pages - Publisher: Butterworth-Heinemann; 5th edition (January, 2019) ... Language: English - ISBN-10: 0081020511 - ISBN-13: 978-0081020517.

Widely adopted around the world, Engineering Materials 1 is a core materials science and engineering text for third- and fourth-year undergraduate students; it provides a broad introduction to the mechanical and environmental properties of materials used in a wide range of engineering applications. The text is deliberately concise, with each chapter designed to cover the content of one lecture. As in previous editions, chapters are arranged in groups dealing with particular classes of properties, each group covering property definitions, measurement, underlying principles, and materials selection techniques. Every group concludes with a chapter of case studies that demonstrate practical engineering problems involving materials. The 5th edition boasts expanded properties coverage, new case studies, more exercises and examples, and all-around improved pedagogy. Engineering Materials 1, Fifth Edition is perfect as a stand-alone text for a one-semester course in engineering materials or a first text with its companion Engineering Materials 2: An Introduction to Microstructures and Processing, in a two-semester course or sequence.
Read

Thursday, June 18, 2020 , ,
Michael Paluszek, Stephanie Thomas ... 252 pages - Publisher: Apress; (February, 2020) ... Language: English - ISBN-10: 1484251237 - ISBN-13: 978-1484251232.

Harness the power of MATLAB for deep-learning challenges. This book provides an introduction to deep learning and using MATLAB's deep-learning toolboxes. You’ll see how these toolboxes provide the complete set of functions needed to implement all aspects of deep learning. Along the way, you'll learn to model complex systems, including the stock market, natural language, and angles-only orbit determination. You’ll cover dynamics and control, and integrate deep-learning algorithms and approaches using MATLAB. You'll also apply deep learning to aircraft navigation using images.

Finally, you'll carry out classification of ballet pirouettes using an inertial measurement unit to experiment with MATLAB's hardware capabilities. What You Will Learn: Explore deep learning using MATLAB and compare it to algorithms + Write a deep learning function in MATLAB and train it with examples + Use MATLAB toolboxes related to deep learning + Implement tokamak disruption prediction.
Read

Tuesday, June 16, 2020 , ,
Ben Stephenson ... 219 pages - Publisher: Springer; 2nd edition (July, 2019) ... Language: English - ISBN-10: 3030188728 - ISBN-13: 978-3030188726.

This student-friendly textbook encourages the development of programming skills through active practice by focusing on exercises that support hands-on learning. The Python Workbook provides a compendium of 186 exercises, spanning a variety of academic disciplines and everyday situations. Solutions to selected exercises are also provided, supported by brief annotations that explain the technique used to solve the problem, or highlight a specific point of Python syntax. This enhanced new edition has been thoroughly updated and expanded with additional exercises, along with concise introductions that outline the core concepts needed to solve them. The exercises and solutions require no prior background knowledge, beyond the material covered in a typical introductory Python programming course.

Features: Uses an accessible writing style and easy-to-follow structure; includes a mixture of classic exercises from the fields of computer science and mathematics, along with exercises that connect to other academic disciplines; presents the solutions to approximately half of the exercises; provides annotations alongside the solutions, which explain the approach taken to solve the problem and relevant aspects of Python syntax; offers a variety of exercises of different lengths and difficulties; contains exercises that encourage the development of programming skills using if statements, loops, basic functions, lists, dictionaries, files, and recursive functions. Undergraduate students enrolled in their first programming course and wishing to enhance their programming abilities will find the exercises and solutions provided in this book to be ideal for their needs.
Read

Monday, June 15, 2020 , ,
Bertrand S. Clarke, Jennifer L. Clarke ... 656 pages - Publisher: Cambridge Univ. Press; (April, 2018) ... Language: English - ISBN-10: 1107028280 - ISBN-13: 978-1107028289.

All scientific disciplines prize predictive success. Conventional statistical analyses, however, treat prediction as secondary, instead focusing on modeling and hence estimation, testing, and detailed physical interpretation, tackling these tasks before the predictive adequacy of a model is established. This book outlines a fully predictive approach to statistical problems based on studying predictors; the approach does not require predictors correspond to a model although this important special case is included in the general approach. Throughout, the point is to examine predictive performance before considering conventional inference. These ideas are traced through five traditional subfields of statistics, helping readers to refocus and adopt a directly predictive outlook. The book also considers prediction via contemporary 'black box' techniques and emerging data types and methodologies where conventional modeling is so difficult that good prediction is the main criterion available for evaluating the performance of a statistical method. Well-documented open-source R code in a Github repository allows readers to replicate examples and apply techniques to other investigations.
Read

Sunday, June 14, 2020 , ,
Bradley Efron, Trevor Hastie ... 495 pages - Publisher: Cambridge Univ. Press; (July, 2016) ... Language: English - ISBN-10: 1107149894 - ISBN-13: 978-1107149892.

The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.
Read

Saturday, June 13, 2020 , ,
Valentina Emilia Balas, Sanjiban Sekhar Roy, Dharmendra Sharma, Pijush Samui ... 389 pages - Publisher: Springer; (March, 2019) ... Language: English - ISBN-10: 3030114783 - ISBN-13: 978-303011478.

This book presents a broad range of deep-learning applications related to vision, natural language processing, gene expression, arbitrary object recognition, driverless cars, semantic image segmentation, deep visual residual abstraction, brain–computer interfaces, big data processing, hierarchical deep learning networks as game-playing artefacts using regret matching, and building GPU-accelerated deep learning frameworks. Deep learning, an advanced level of machine learning technique that combines class of learning algorithms with the use of many layers of nonlinear units, has gained considerable attention in recent times. Unlike other books on the market, this volume addresses the challenges of deep learning implementation, computation time, and the complexity of reasoning and modeling different type of data. As such, it is a valuable and comprehensive resource for engineers, researchers, graduate students and Ph.D. scholars.
Read

Friday, June 12, 2020 , ,
Ryosuke Kitamura, Kazunari Sako ... 188 pages - Publisher: CRC Press; (August, 2019) ... Language: English - AmazonSIN: B07W86B2PY.

Soil is fundamentally a multi-phase material – consisting of solid particles, water and air. In soil mechanics and geotechnical engineering it is widely treated as an elastic, elastoplastic or visco-elastoplastic material, and consequently regarded as a continuum body. However, this book explores an alternative approach, considering soil as a multi-phase and discrete material and applying basic Newtonian mechanics rather than analytical mechanics. It applies microscopic models to the solid phase and fluid phases, and then introduces probability theory and statistics to derive average physical quantities which correspond to the soil‘s macroscopic physical properties such as void ratio and water content. This book is particularly focused on the mechanical behaviour of dry, partially saturated and full saturated sandy soil, as much of the physicochemical microscopic characteristic of clayey soil is still not clear. It explores the inter-particle forces at the point of contact of soil particles and the resultant inter-particle stresses, instead of the total stress and effective stress which are studied in mainstream soil mechanics. Deformation and strength behaviour, soil-water characteristic curves, and permeability coefficients of water and air are then derived simply from grain size distribution, soil particle density, void ratio and water content. A useful reference for consultants, professional engineers, researchers and public sector organisations involved in unsaturated soil tests. Advanced undergraduate and postgraduate students on Unsaturated Soil Mechanics courses will also find it a valuable text to study.
Read

Thursday, June 11, 2020 , ,
Ralf T. Kreutzer, Marie Sirrenberg ... 313 pages - Publisher: Springer; (September, 2019) ... Language: English - ISBN-10: 3030252701 - ISBN-13: 978-3030252700.

Artificial Intelligence (AI) will change the lives of people and businesses more fundamentally than many people can even imagine today. This book illustrates the importance of AI in an era of digitalization. It introduces the foundations of AI and explains its benefits and challenges for companies and entire industries. In this regard, AI is approached not just as yet another technology, but as a fundamental innovation, which will spread into all areas of the economy and life, and will disrupt business processes and business models in the years to come. In turn, the book assesses the potential that AI holds, and clarifies the framework that is necessary for pursuing a responsible approach to AI. In a series of best-practice cases, the book subsequently highlights a broad range of sectors and industries, from production to services; from customer service to marketing and sales; and in industries like retail, health care, energy, transportation and many more. In closing, a dedicated chapter outlines a roadmap for a specific corporate AI journey.
Read

Wednesday, June 10, 2020 , , ,
Naiyang Deng, Yingjie Tian, Chunhua Zhang ... 363 pages - Publisher: Chapman and Hall/CRC; (December, 2012) ... Language: English - AmazonSIN: B00OD4GXCI.

Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions presents an accessible treatment of the two main components of support vector machines (SVMs)—classification problems and regression problems. The book emphasizes the close connection between optimization theory and SVMs since optimization is one of the pillars on which SVMs are built. The authors share insight on many of their research achievements. They give a precise interpretation of statistical leaning theory for C-support vector classification. They also discuss regularized twin SVMs for binary classification problems, SVMs for solving multi-classification problems based on ordinal regression, SVMs for semi-supervised problems, and SVMs for problems with perturbations. To improve readability, concepts, methods, and results are introduced graphically and with clear explanations. For important concepts and algorithms, such as the Crammer-Singer SVM for multi-class classification problems, the text provides geometric interpretations that are not depicted in current literature. Enabling a sound understanding of SVMs, this book gives beginners as well as more experienced researchers and engineers the tools to solve real-world problems using SVMs.
Read

Tuesday, June 09, 2020 , , ,
Kieth A. Carlson, Jennifer R. Winquist ... 656 pages - Publisher: SAGE Publications, Inc; 2nd edition (February, 2017) ... Language: English - ISBN-10: 148337873X - ISBN-13: 978-1483378732.

An Introduction to Statistics: An Active Learning Approach, Second Edition by Kieth A. Carlson and Jennifer R. Winquist takes a unique, active approach to teaching and learning introductory statistics that allows students to discover and correct their misunderstandings as chapters progress rather than at their conclusion. Empirically-developed, self-correcting activities reinforce and expand on fundamental concepts, targeting and holding students’ attention. Based on contemporary memory research, this learner-centered approach leads to better long-term retention through active engagement while generating explanations. Along with carefully placed reading questions, this edition includes learning objectives, realistic research scenarios, practice problems, self-test questions, problem sets, and practice tests to help students become more confident in their ability to perform statistics.
Read

Monday, June 08, 2020 , ,
Narayan C. Giri ... 537 pages - Publisher: CRC Press; 2nd edition (January, 2019) ... Language: English - AmazonSIN: B07M7XVF7J.

Beginning with the historical background of probability theory, this thoroughly revised text examines all important aspects of mathematical probability - including random variables, probability distributions, characteristic and generating functions, stochatic convergence, and limit theorems - and provides an introduction to various types of statistical problems, covering the broad range of statistical inference.; Requiring a prerequisite in calculus for complete understanding of the topics discussed, the Second Edition contains new material on: univariate distributions; multivariate distributions; large-sample methods; decision theory; and applications of ANOVA.; A primary text for a year-long undergraduate course in statistics (but easily adapted for a one-semester course in probability only), Introduction to Probability and Statistics is for undergraduate students in a wide range of disciplines-statistics, probability, mathematics, social science, economics, engineering, agriculture, biometry, and education.
Read

Sunday, June 07, 2020 , ,
Larry Wasserman ... 442 pages - Publisher: Springer; (September, 2004) ... Language: English - ISBN-10: 0387402721 - ISBN-13: 978-0387402727.

Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data.
Read

Saturday, June 06, 2020 , ,
Kuniaki Sato, Yoshiaki Iwasa ... 204 pages - Publisher: Springer; (June, 2011) ... Language: English - AmazonSIN: B000PY48N6.

The groundwater science and engineering has been closely connected with various fields (1) Groundwater Hydrology, (2) Groundwater Hydraulics or Geohydraulics, (3) Fluid Dynamics in Porous Media, (4) Groundwater Quality Engineering, (5) Soil Physics, and (6) Hydrogeology or Geohydrology. The purpose of the book is to present an update textbook of groundwater hydraulics, which includes all of basic items in above-mentioned fields, to students (of graduate school), researchers and practitioners. The students and beginners who intend to specialize in groundwater hydraulics through one semester will master contents of the book.
Read

Friday, June 05, 2020 , ,
Ethem Alpaydin ... 640 pages - Publisher: Phi; 3rd edition (2015) ... Language: English - ISBN-10: 8120350782 - ISBN-13: 978-8120350786.

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. Subjects include supervised learning; Bayesian decision theory; parametric, semi-parametric, and nonparametric methods; multivariate analysis; hidden Markov models; reinforcement learning; kernel machines; graphical models; Bayesian estimation; and statistical testing.

Machine learning is rapidly becoming a skill that computer science students must master before graduation. The third edition of Introduction to Machine Learning reflects this shift, with added support for beginners, including selected solutions for exercises and additional example data sets (with code available online). Other substantial changes include discussions of outlier detection; ranking algorithms for perceptrons and support vector machines; matrix decomposition and spectral methods; distance estimation; new kernel algorithms; deep learning in multilayered perceptrons; and the nonparametric approach to Bayesian methods. All learning algorithms are explained so that students can easily move from the equations in the book to a computer program. The book can be used by both advanced undergraduates and graduate students. It will also be of interest to professionals who are concerned with the application of machine learning methods. Introduction to Machine Learning can be used by advanced undergraduates and graduate students who have completed courses in computer programming, probability, calculus, and linear algebra. It will also be of interest to engineers in the field who are concerned with the application of machine learning methods.
Read

Thursday, June 04, 2020 , ,
Chris Chatfield, A. Collins ... 248 pages - Publisher: Chapman and Hall/CRC; (May, 1981) ... Language: English - ISBN-10: 9780412160400 - ISBN-13: 978-0412160400.

This book provides an introduction to the analysis of multivariate data.It describes multivariate probability distributions, the preliminary analysisof a large -scale set of data, princ iple component and factor analysis,traditional normal theory material, as well as multidimensional scaling andcluster analysis.Introduction to Multivariate Analysis provides a reasonable blend oftheory and practice. Enough theory is given to introduce the concepts andto make the topics mathematically interesting. In addition the authors discussthe use (and misuse) of the techniques in pra ctice and present appropriatereal-life examples from a variety of areas includ ing agricultural research,soc iology and crim inology. The book should be suitable both for researchworkers and as a text for students taking a course on multivariate analysis.
Read

Wednesday, June 03, 2020 , ,
Sadanori Konishi ... 338 pages - Publisher: Chapman and Hall/CRC; (June, 2014) ... Language: English - ISBN-10: 1466567287 - ISBN-13: 978-1466567283.

Introduction to Multivariate Analysis: Linear and Nonlinear Modeling shows how multivariate analysis is widely used for extracting useful information and patterns from multivariate data and for understanding the structure of random phenomena. Along with the basic concepts of various procedures in traditional multivariate analysis, the book covers nonlinear techniques for clarifying phenomena behind observed multivariate data. It primarily focuses on regression modeling, classification and discrimination, dimension reduction, and clustering. The text thoroughly explains the concepts and derivations of the AIC, BIC, and related criteria and includes a wide range of practical examples of model selection and evaluation criteria. To estimate and evaluate models with a large number of predictor variables, the author presents regularization methods, including the L1 norm regularization that gives simultaneous model estimation and variable selection. For advanced undergraduate and graduate students in statistical science, this text provides a systematic description of both traditional and newer techniques in multivariate analysis and machine learning. It also introduces linear and nonlinear statistical modeling for researchers and practitioners in industrial and systems engineering, information science, life science, and other areas.
Read

Tuesday, June 02, 2020 , ,
Roger Koenker, Victor Chernozhukov, Xuming He, Limin Peng ... 483 pages - Publisher: Chapman and Hall/CRC; (October, 2017) ... Language: English - AmazonSIN: B076DG4VR4.

Quantile regression constitutes an ensemble of statistical techniques intended to estimate and draw inferences about conditional quantile functions. Median regression, as introduced in the 18th century by Boscovich and Laplace, is a special case. In contrast to conventional mean regression that minimizes sums of squared residuals, median regression minimizes sums of absolute residuals; quantile regression simply replaces symmetric absolute loss by asymmetric linear loss. Since its introduction in the 1970's by Koenker and Bassett, quantile regression has been gradually extended to a wide variety of data analytic settings including time series, survival analysis, and longitudinal data. By focusing attention on local slices of the conditional distribution of response variables it is capable of providing a more complete, more nuanced view of heterogeneous covariate effects. Applications of quantile regression can now be found throughout the sciences, including astrophysics, chemistry, ecology, economics, finance, genomics, medicine, and meteorology. Software for quantile regression is now widely available in all the major statistical computing environments.

The objective of this volume is to provide a comprehensive review of recent developments of quantile regression methodology illustrating its applicability in a wide range of scientific settings. The intended audience of the volume is researchers and graduate students across a diverse set of disciplines.
Read

Monday, June 01, 2020 , ,
Wolfgang Karl Härdle, Léopold Simar ... 558 pages - Publisher: Springer; 5th edition(November, 2019) ... Language: English - ISBN-10: 3030260054 - ISBN-13: 978-3030260057.

This textbook presents the tools and concepts used in multivariate data analysis in a style accessible for non-mathematicians and practitioners. All chapters include practical exercises that highlight applications in different multivariate data analysis fields, and all the examples involve high to ultra-high dimensions and represent a number of major fields in big data analysis.

For this new edition, the book has been updated and extensively revised and now includes an extended chapter on cluster analysis. All solutions to the exercises are supplemented by R and MATLAB or SAS computer code and can be downloaded from the Quantlet platform. Practical exercises from this book and their solutions can also be found in the accompanying Springer book by W.K. Härdle and Z. Hlávka: Multivariate Statistics - Exercises and Solutions.
Read

Sunday, May 31, 2020 , ,
Roman Vershynin ... 296 pages - Publisher: Cambridge Univ.Press; (September, 2018) ... Language: English - ISBN-10: 1108415199 - ISBN-13: 978-1108415194.

High-dimensional probability offers insight into the behavior of random vectors, random matrices, random subspaces, and objects used to quantify uncertainty in high dimensions. Drawing on ideas from probability, analysis, and geometry, it lends itself to applications in mathematics, statistics, theoretical computer science, signal processing, optimization, and more. It is the first to integrate theory, key tools, and modern applications of high-dimensional probability. Concentration inequalities form the core, and it covers both classical results such as Hoeffding's and Chernoff's inequalities and modern developments such as the matrix Bernstein's inequality. It then introduces the powerful methods based on stochastic processes, including such tools as Slepian's, Sudakov's, and Dudley's inequalities, as well as generic chaining and bounds based on VC dimension. A broad range of illustrations is embedded throughout, including classical and modern results for covariance estimation, clustering, networks, semidefinite programming, coding, dimension reduction, matrix completion, machine learning, compressed sensing, and sparse regression.
Read

Saturday, May 30, 2020 , , ,
Jeff Heaton ... 244 pages - Publisher: Heaton Research, Inc.; (August, 2014) ... Language: English - AmazonSIN: B00MYLNLSQ.

Nature can be a great source of inspiration for artificial intelligence algorithms because its technology is considerably more advanced than our own. Among its wonders are strong AI, nanotechnology, and advanced robotics. Nature can therefore serve as a guide for real-life problem solving. In this book, you will encounter algorithms influenced by ants, bees, genomes, birds, and cells that provide practical methods for many types of AI situations. Although nature is the muse behind the methods, we are not duplicating its exact processes. The complex behaviors in nature merely provide inspiration in our quest to gain new insights about data.

Artificial Intelligence for Humans is a book series meant to teach AI to those readers who lack an extensive mathematical background. The reader only needs knowledge of basic college algebra and computer programming. Additional topics are thoroughly explained. Every chapter also includes a programming example. Examples are currently provided in Java, C#, and Python. Other languages are planned. No knowledge of biology is needed to read this book.
Read

Friday, May 29, 2020 , ,
Debbie L. Hahs-Vaughn ... 662 pages - Publisher: Routledge; (November, 2016) ... Language: English - ISBN-10: 0415842360 - ISBN-13: 978-0415842365.

More comprehensive than other texts, this new book covers the classic and cutting edge multivariate techniques used in today’s research. Ideal for courses on multivariate statistics/analysis/design, advanced statistics or quantitative techniques taught in psychology, education, sociology, and business, the book also appeals to researchers with no training in multivariate methods. Through clear writing and engaging pedagogy and examples using real data, Hahs-Vaughn walks students through the most used methods to learn why and how to apply each technique. A conceptual approach with a higher than usual text-to-formula ratio helps reader’s master key concepts so they can implement and interpret results generated by today’s sophisticated software. Annotated screenshots from SPSS and other packages are integrated throughout. Designed for course flexibility, after the first 4 chapters, instructors can use chapters in any sequence or combination to fit the needs of their students. Each chapter includes a ‘mathematical snapshot’ that highlights the technical components of each procedure, so only the most crucial equations are included.

Highlights include: -Outlines, key concepts, and vignettes related to key concepts preview what’s to come in each chapter. -Examples using real data from education, psychology, and other social sciences illustrate key concepts. -Extensive coverage of assumptions including tables, the effects of their violation, and how to test for each technique. -Conceptual, computational, and interpretative problems mirror the real-world problems students encounter in their studies and careers. -A focus on data screening and power analysis with attention on the special needs of each particular method. -Instructions for using SPSS via screenshots and annotated output along with HLM, Mplus, LISREL, and G*Power where appropriate, to demonstrate how to interpret results. -Templates for writing research questions and APA-style write-ups of results which serve as models. -Propensity score analysis chapter that demonstrates the use of this increasingly popular technique. -A review of matrix algebra for those who want an introduction (prerequisites include an introduction to factorial ANOVA, ANCOVA, and simple linear regression, but knowledge of matrix algebra is not assumed).
Read

Thursday, May 28, 2020 , , ,
Anand J. Kulkarni, Suresh Chandra Satapathy ... 197 pages - Publisher: Springer; (November, 2019) ... Language: English - AmazonSIN: B0825P5H9C.

This book discusses one of the major applications of artificial intelligence: the use of machine learning to extract useful information from multimodal data. It discusses the optimization methods that help minimize the error in developing patterns and classifications, which further helps improve prediction and decision-making. The book also presents formulations of real-world machine learning problems, and discusses AI solution methodologies as standalone or hybrid approaches. Lastly, it proposes novel metaheuristic methods to solve complex machine learning problems. Featuring valuable insights, the book helps readers explore new avenues leading toward multidisciplinary research discussions.
Read

Wednesday, May 27, 2020 , , ,
Pasquale Giovine, Paolo Maria Mariano, Giuseppe Mortara ... 430 pages - Publisher: Birkhäuser; (March, 2019) ... Language: English - AmazonSIN: B07QQV28CH.

This special issue collects selected contributions (excluding general lectures) of a Symposium on "Micro to MACRO Mathematical Modelling in Soil Mechanics", which took place at the University of Reggio Calabria, Italy, from May 29th to June 1st, 2018. The Symposium provided an opportunity to enhance the scientific debate on the construction of mathematical models for the description of the physical behaviour of soils, as well as on the suggestions provided by the micro-mechanical observation of the matter. The focus was on the comparison between the appropriateness of models and the need of mathematics to obtain rigorous results, which involves know-how from applied mathematical physics, geotechnical engineering and mechanics of solids.
Read

Tuesday, May 26, 2020 , ,
Loui McCurley ... 328 pages - Publisher: Wiley; (June, 2016) ... Language: English - ISBN-10: 111885960X - ISBN-13: 978-1118859605.

The comprehensive guide to performing work at height more safely and at less cost: In the present economy, employers around the globe are working harder than ever to maximize safety in work at height with a careful eye toward fiscal responsibility.   In the drive to do more with less (workers, money, time, resources, etc.) rope access offers the opportunity to raise the bar in both safety and cost-efficiency.  This specialized mode of access and protection is a solution that site owners and managers frequently turn to for putting specially trained, certified technicians in hard-to-reach places with maximum safety and minimal cost. Professional Rope Access: A Guide to Working Safely at Height is a comprehensive guide that employers, safety managers, trainers, and technicians can use to develop and maintain a rope access program that will get more work accomplished at a lower cost and with greater safety than conventional methods can achieve.

Professional Rope Access features topics on: Planning For Rope Access such as personnel selection and training and Equipment for Rigging + Skills For The Rope Access Technician including Rigging Concepts, Advanced Techniques, Powered Devices, and Rescue + Program Administration from Writing a Work Order through Equipment Inspection & Care.
Read

Tuesday, May 26, 2020 , ,
Vikrant Sharma, Atul Kumar, N. S. Baruaole, Mukesh Kumar ... 292 pages - Publisher: Alpha Science; (April, 2018) ... Language: English - ISBN-10: 1783323566 - ISBN-13: 978-1783323562.

Statics is one of the most important and fundamental courses in engineering mechanics. The objective of this book is to impart knowledge of fundamental concepts and to gain skill of identifying, formulating and solving engineering problems and also to apply concepts of statics in solving real life problems. The book starts with an introduction to mechanics and goes on to cover concepts of statics like system of forces, equilibrium, analysis of structures, centroid, moment of inertial, friction and stress strain. The topics are covered in an easy to understand manner. Since problem solving is critical in engineering mechanics, the solutions to the problems are given in a systematic and step-wise manner.
Read

Monday, May 25, 2020 , , ,
Svein Linge, Hans Petter Langtangen ... 216 pages - Publisher: Springer; (August, 2016) ... Language: English - AmazonSIN: B078YHJHL2.

This book presents computer programming as a key method for solving mathematical problems. There are two versions of the book, one for MATLAB and one for Python. The book was inspired by the Springer book TCSE 6: A Primer on Scientific Programming with Python (by Langtangen), but the style is more accessible and concise, in keeping with the needs of engineering students. The book outlines the shortest possible path from no previous experience with programming to a set of skills that allows the students to write simple programs for solving common mathematical problems with numerical methods in engineering and science courses. The emphasis is on generic algorithms, clean design of programs, use of functions, and automatic tests for verification.
Read

Sunday, May 24, 2020 , ,
Fouad Bennis, Rajib Kumar Bhattacharjya ... 502 pages - Publisher: Springer; (January, 2020) ... Language: English - ASIN: B0843NX5CT.

This book gathers together a set of chapters covering recent development in optimization methods that are inspired by nature. The first group of chapters describes in detail different meta-heuristic algorithms, and shows their applicability using some test or real-world problems. The second part of the book is especially focused on advanced applications and case studies. They span different engineering fields, including mechanical, electrical and civil engineering, and earth/environmental science, and covers topics such as robotics, water management, process optimization, among others. The book covers both basic concepts and advanced issues, offering a timely introduction to nature-inspired optimization method for newcomers and students, and a source of inspiration as well as important practical insights to engineers and researchers.
Read

Friday, May 22, 2020 , ,
Sanford Weisberg ... 368 pages - Publisher: Wiley; 4th edition (December, 2013) ... Language: English - ISBN-10: 9781118386088 - ISBN-13: 978-1118386088.

The Fourth Edition of Applied Linear Regression provides a thorough update of the basic theory and methodology of linear regression modeling. Demonstrating the practical applications of linear regression analysis techniques, the Fourth Edition uses interesting, real-world exercises and examples. Stressing central concepts such as model building, understanding parameters, assessing fit and reliability, and drawing conclusions, the new edition illustrates how to develop estimation, confidence, and testing procedures primarily through the use of least squares regression. 

While maintaining the accessible appeal of each previous edition, Applied Linear Regression, Fourth Edition features: Graphical methods stressed in the initial exploratory phase, analysis phase, and summarization phase of an analysis + In-depth coverage of parameter estimates in both simple and complex models, transformations, and regression diagnostics + Newly added material on topics including testing, ANOVA, and variance assumptions + Updated methodology, such as bootstrapping, cross-validation binomial and Poisson regression, and modern model selection methods. Applied Linear Regression, Fourth Edition is an excellent textbook for upper-undergraduate and graduate-level students, as well as an appropriate reference guide for practitioners and applied statisticians in engineering, business administration, economics, and the social sciences.
Read

Thursday, May 21, 2020 , , ,
Elliot J. Gindis, Robert C. Kaebisch ... 592 pages - Publisher: Academic Press; (July, 2019) ... Language: English - AmazonSIN: B07YRQH56P.

Up and Running with AutoCAD 2020 uses a combination of step-by-step instruction, examples and insightful explanations to emphasize core concepts and practical application of AutoCAD in engineering, architecture, and design. Equally useful in instructor-led classroom training, self-study, or as a reference, the book is written with the user in mind by long-time professional AutoCAD instructors based on what works in the industry and the classroom. The book focuses on 2D drafting and design, making it more appropriate for a one-semester course.

Contents: Strips away complexities and reduces learning AutoCAD to easy-to-understand concepts. + Teaches the essentials of AutoCAD first, immediately building student confidence. + Provides all basic commands documented step-by-step: What the student inputs and how AutoCAD responds is spelled out in discrete and clear steps with numerous screenshot.s + Presents extensive supporting graphics and a summary with a self-test section and topic specific drawing exercises at the end of each chapter. + Covers the essentials of 2D AutoCAD, updated for the 2020 release.
Read

Wednesday, May 20, 2020 , ,
Mario Paz, Young Hoon Kim ... 634 pages - Publisher: Springer; 6th edition (November, 2018) ... Language: English - ISBN-10: 3319947427 - ISBN-13: 978-3319947426.

The sixth edition of Structural Dynamics: Theory and Computation is the complete and comprehensive text in the field. It presents modern methods of analysis and techniques adaptable to computer programming clearly and easily. The book is ideal as a text for advanced undergraduates or graduate students taking a first course in structural dynamics. It is arranged in such a way that it can be used for a one- or two-semester course, or span the undergraduate and graduate levels. In addition, this text will serve the practicing engineer as a primary reference. The text differs from the standard approach of other presentations in which topics are ordered by their mathematical complexity. This text is organized by the type of structural modeling. The author simplifies the subject by presenting a single degree-of-freedom system in the first chapters, then moves to systems with many degrees-of-freedom in the following chapters. Finally, the text moves to applications of the first chapters and special topics in structural dynamics.

This revised textbook intends to provide enhanced learning materials for students to learn structural dynamics, ranging from basics to advanced topics, including their application. When a line-by-line programming language is included with solved problems, students can learn course materials easily and visualize the solved problems using a program. Among several programming languages, MATLAB has been adopted by many academic institutions across several disciplines. Many educators and students in the U.S. and many international institutions can readily access MATLAB, which has an appropriate programming language to solve and simulate problems in the textbook. It effectively allows matrix manipulations and plotting of data. Therefore, multi-degree-of freedom problems can be solved in conjunction with the finite element method using MATLAB. 

The revised version will include: Solved 34 examples in Chapters 1 through 22 along with MATLAB codes. + Basics of earthquake design with current design codes (ASCE 7-16 and IBC 2018). + Additional figures obtained from MATLAB codes to illustrate time-variant structural behavior and dynamic characteristics (e.g., time versus displacement and spectral chart).
Read

Tuesday, May 19, 2020 , , ,
B. S. Grewal ... 1597 pages - Publisher: Mercury Learning and Information; (September, 2018) ... Language: English - AmazonSIN: B07HHJ611G.

This book is intended as an introduction to numerical methods for scientists and engineers. Providing an excellent balance of theoretical and applied topics, it shows the numerical methods used with C, C++, and MATLAB.

Brief Table of Contents: 1: Approximations and Errors in Computation. 2: Solution of Algebraic and Transcendental Equations. 3: Solution of Simultaneous Algebraic Equations. 4: Matrix Inversion and the Eigenvalue Problem. 5: Empirical Laws and Curve-Fitting. 6: Finite Differences. 7: Interpolation. 8: Numerical Differentiation and Integration. 9: Difference Equations. 10: Numerical Solution of Ordinary Differential Equations. 11: Numerical Solution of Partial Differential Equations. 12: Linear Programming. 13: A Brief Review of Computers. 14: Numerical Methods Using C. 15: Numerical Methods Using C++. 16: Numerical Methods Using MATLAB. Appendix A: Useful Information. Appendix B: Answers to Exercises. Appendix C: Bibliography. Index.
Read

Monday, May 18, 2020 , , , ,
Thomas Cleff ... 497 pages - Publisher: Springer; (July, 2019) ... Language: English - AmazonSIN: B07V82V6L6.

This textbook will familiarize students in economics and business, as well as practitioners, with the basic principles, techniques, and applications of applied statistics, statistical testing, and multivariate data analysis. Drawing on practical examples from the business world, it demonstrates the methods of univariate, bivariate, and multivariate statistical analysis. The textbook covers a range of topics, from data collection and scaling to the presentation and simple univariate analysis of quantitative data, while also providing advanced analytical procedures for assessing multivariate relationships. Accordingly, it addresses all topics typically covered in university courses on statistics and advanced applied data analysis. In addition, it does not limit itself to presenting applied methods, but also discusses the related use of Excel, SPSS, and Stata.
Read

Sunday, May 17, 2020 , , ,
Ulrich Kohler, Frauke Kreuter ... 497 pages - Publisher: Stata Press; 3rd edition (August, 2012) ... Language: English - ISBN-10: 1597181102 - ISBN-13: 978-1597181105.

Data Analysis Using Stata, Third Edition is a comprehensive introduction to both statistical methods and Stata. Beginners will learn the logic of data analysis and interpretation and easily become self-sufficient data analysts. Readers already familiar with Stata will find it an enjoyable resource for picking up new tips and tricks. The book is written as a self-study tutorial and organized around examples. It interactively introduces statistical techniques such as data exploration, description, and regression techniques for continuous and binary dependent variables. Step by step, readers move through the entire process of data analysis and in doing so learn the principles of Stata, data manipulation, graphical representation, and programs to automate repetitive tasks. This third edition includes advanced topics, such as factor-variables notation, average marginal effects, standard errors in complex survey, and multiple imputation in a way, that beginners of both data analysis and Stata can understand. Using data from a longitudinal study of private households, the authors provide examples from the social sciences that are relatable to researchers from all disciplines. The examples emphasize good statistical practice and reproducible research. Readers are encouraged to download the companion package of datasets to replicate the examples as they work through the book. Each chapter ends with exercises to consolidate acquired skills.
Read

Saturday, May 16, 2020 , ,
Max Bramer ... 544 pages - Publisher: Springer; 3rd edition (November, 2016) ... Language: English - AmazonSIN: B01N3LZ1KI.

This book explains and explores the principal techniques of Data Mining, the automatic extraction of implicit and potentially useful information from data, which is increasingly used in commercial, scientific and other application areas. It focuses on classification, association rule mining and clustering. Each topic is clearly explained, with a focus on algorithms not mathematical formalism, and is illustrated by detailed worked examples. The book is written for readers without a strong background in mathematics or statistics and any formulae used are explained in detail. It can be used as a textbook to support courses at undergraduate or postgraduate levels in a wide range of subjects including Computer Science, Business Studies, Marketing, Artificial Intelligence, Bioinformatics and Forensic Science. As an aid to self study, this book aims to help general readers develop the necessary understanding of what is inside the 'black box' so they can use commercial data mining packages discriminatingly, as well as enabling advanced readers or academic researchers to understand or contribute to future technical advances in the field. Each chapter has practical exercises to enable readers to check their progress. A full glossary of technical terms used is included. This expanded third edition includes detailed descriptions of algorithms for classifying streaming data, both stationary data, where the underlying model is fixed, and data that is time-dependent, where the underlying model changes from time to time - a phenomenon known as concept drift.
Read
Subscribe to: Posts (Atom)
Geo.Engineerings © since June 10, 2015.
Believe in Power of ENGINEERINGS.

Contact Form

Name

Email *

Message *

Powered by Blogger.