Geo.Engineerings

Alan Graham ... 320 pages - Publisher: Teach Yourself; (April, 2017) ... Language: English - AmazonSIN: B01LZ6WZXS.

Do you need to gain confidence with handling numbers and formulae? Do you want a clear, step-by-step guide to the key concepts and principles of statistics? Nearly all aspects of our lives can be subject to statistical analysis. Statistics: An Introduction shows you how to interpret, analyze and present figures. Assuming minimal knowledge of maths and using examples from a wide variety of everyday contexts, this book makes often complex concepts and techniques easy to get to grips with. This new edition has been fully updated. Whether you want to understand the statistics that you are bombarded with every day or are a student or professional coming to statistics from a wide range of disciplines, Statistics: An Introduction covers it all.

Nilanjan Dey ... 266 pages - Publisher: Springer; (November, 2019) ... Language: English - AmazonSIN: B0818MWNQJ.

The book discusses advantages of the firefly algorithm over other well-known metaheuristic algorithms in various engineering studies. The book provides a brief outline of various application-oriented problem solving methods, like economic emission load dispatch problem, designing a fully digital controlled reconfigurable switched beam nonconcentric ring array antenna, image segmentation, span minimization in permutation flow shop scheduling, multi-objective load dispatch problems, image compression, etc., using FA and its variants. It also covers the use of the firefly algorithm to select features, as research has shown that the firefly algorithm generates precise and optimal results in terms of time and optimality. In addition, the book also explores the potential of the firefly algorithm to provide a solution to traveling salesman problem, graph coloring problem, etc.

Seth Weidman ... 253 pages - Publisher: O'Reilly Media; (September, 2019) ... Language: English - AmazonSIN: B07XL53Y4C.

With the resurgence of neural networks in the 2010s, deep learning has become essential for machine learning practitioners and even many software engineers. This book provides a comprehensive introduction for data scientists and software engineers with machine learning experience. You’ll start with deep learning basics and move quickly to the details of important advanced architectures, implementing everything from scratch along the way. Author Seth Weidman shows you how neural networks work using a first principles approach. You’ll learn how to apply multilayer neural networks, convolutional neural networks, and recurrent neural networks from the ground up. With a thorough understanding of how neural networks work mathematically, computationally, and conceptually, you’ll be set up for success on all future deep learning projects.

This book provides: Extremely clear and thorough mental models—accompanied by working code examples and mathematical explanations—for understanding neural networks + Methods for implementing multilayer neural networks from scratch, using an easy-to-understand object-oriented framework + Working implementations and clear-cut explanations of convolutional and recurrent neural networks + Implementation of these neural network concepts using the popular PyTorch framework.

T. S. Nagaraj ...708 pages -Publisher: Elsevier Science; (January, 1993) ... Language: English - ISBN-10: 0444889116 - ISBN-13: 978-0444889119.

Soils, rocks and concrete are the principal materials a civil engineer encounters in practice. This book deals with the material analogies, their implications in property characterization, giving attention to similar as well as dissimilar methods in respect of each of these three materials. It provides an integrated, systematic approach for realistic assessment of engineering properties of soils, rocks and concrete. Geotechnical engineers, civil engineers and materials scientists will be interested in this volume.

Richard S. Sutton, Andrew G. Barto ... 532 pages - Publisher: A Bradford Book; 2nd edition (October, 2018) ... Language: English - AmazonSIN: B07JN1QFW5.

The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence: Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics.

Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.

Eugene Charniak ... 192 pages - Publisher: The MIT Press; (January, 2019) ... Language: English - AmazonSIN: B07PGRZXN8.

This concise, project-driven guide to deep learning takes readers through a series of program-writing tasks that introduce them to the use of deep learning in such areas of artificial intelligence as computer vision, natural-language processing, and reinforcement learning. The author, a longtime artificial intelligence researcher specializing in natural-language processing, covers feed-forward neural nets, convolutional neural nets, word embeddings, recurrent neural nets, sequence-to-sequence learning, deep reinforcement learning, unsupervised models, and other fundamental concepts and techniques. Students and practitioners learn the basics of deep learning by working through programs in Tensorflow, an open-source machine learning framework. “I find I learn computer science material best by sitting down and writing programs,” the author writes, and the book reflects this approach.

Each chapter includes a programming project, exercises, and references for further reading. An early chapter is devoted to Tensorflow and its interface with Python, the widely used programming language. Familiarity with linear algebra, multivariate calculus, and probability and statistics is required, as is a rudimentary knowledge of programming in Python. The book can be used in both undergraduate and graduate courses; practitioners will find it an essential reference.

Jesús Rogel-Salazar ... 420 pages - Publisher: Chapman and Hall/CRC; (May, 2020) ... Language: English - AmazonSIN: B0883XB13B.

Advanced Data Science and Analytics with Python enables data scientists to continue developing their skills and apply them in business as well as academic settings. The subjects discussed in this book are complementary and a follow-up to the topics discussed in Data Science and Analytics with Python. The aim is to cover important advanced areas in data science using tools developed in Python such as SciKit-learn, Pandas, Numpy, Beautiful Soup, NLTK, NetworkX and others. The model development is supported by the use of frameworks such as Keras, TensorFlow and Core ML, as well as Swift for the development of iOS and MacOS applications.

Features: Targets readers with a background in programming, who are interested in the tools used in data analytics and data science + Uses Python throughout + Presents tools, alongside solved examples, with steps that the reader can easily reproduce and adapt to their needs + Focuses on the practical use of the tools rather than on lengthy explanations + Provides the reader with the opportunity to use the book whenever needed rather than following a sequential path.

The book can be read independently from the previous volume and each of the chapters in this volume is sufficiently independent from the others, providing flexibility for the reader. Each of the topics addressed in the book tackles the data science workflow from a practical perspective, concentrating on the process and results obtained. The implementation and deployment of trained models are central to the book. Time series analysis, natural language processing, topic modelling, social network analysis, neural networks and deep learning are comprehensively covered. The book discusses the need to develop data products and addresses the subject of bringing models to their intended audiences – in this case, literally to the users’ fingertips in the form of an iPhone app.

Kumar Molugaram, G. Shanker Rao ... 538 pages - Publisher: Butterworth-Heinemann; (March, 2017) ... Language: English.

Statistical Techniques for Transportation Engineering is written with a systematic approach in mind and covers a full range of data analysis topics, from the introductory level (basic probability, measures of dispersion, random variable, discrete and continuous distributions) through more generally used techniques (common statistical distributions, hypothesis testing), to advanced analysis and statistical modeling techniques (regression, AnoVa, and time series). The book also provides worked out examples and solved problems for a wide variety of transportation engineering challenges.

Andreas Öchsner ... 606 pages - Publisher: Springer; 2nd edition (January, 2020) ... Language: English - AmazonSIN: B083GNPFGW.

This book is the 2nd edition of an introduction to modern computational mechanics based on the finite element method. It includes more details on the theory, more exercises, and more consistent notation; in addition, all pictures have been revised. Featuring more than 100 pages of new material, the new edition will help students succeed in mechanics courses by showing them how to apply the fundamental knowledge they gained in the first years of their engineering education to more advanced topics. In order to deepen readers’ understanding of the equations and theories discussed, each chapter also includes supplementary problems. These problems start with fundamental knowledge questions on the theory presented in the respective chapter, followed by calculation problems. In total, over 80 such calculation problems are provided, along with brief solutions for each. This book is especially designed to meet the needs of Australian students, reviewing the mathematics covered in their first two years at university. The 13-week course comprises three hours of lectures and two hours of tutorials per week.

Sayavur I. Bakhtiyarov ... 125 pages - Publisher: Morgan&Claypool; (October, 2017) ... Language: English - ISBN-10: 168173611X - ISBN-13: 978-1681736112.

Engineering mechanics is one of the fundamental branches of science that is important in the education of professional engineers of any major. Most of the basic engineering courses, such as mechanics of materials, fluid and gas mechanics, machine design, mechatronics, acoustics, vibrations, etc. are based on engineering mechanics courses. In order to absorb the materials of engineering mechanics, it is not enough to consume just theoretical laws and theorems—a student also must develop an ability to solve practical problems. Therefore, it is necessary to solve many problems independently. This book is a part of a four-book series designed to supplement the engineering mechanics courses. This series instructs and applies the principles required to solve practical engineering problems in the following branches of mechanics: statics, kinematics, dynamics, and advanced kinetics. Each book contains between 6 and 8 topics on its specific branch and each topic features 30 problems to be assigned as homework, tests, and/or midterm/final exams with the consent of the instructor. A solution of one similar sample problem from each topic is provided. This first book contains seven topics of statics, the branch of mechanics concerned with the analysis of forces acting on construction systems without an acceleration (a state of the static equilibrium). The book targets the undergraduate students of the sophomore/junior level majoring in science and engineering.

Terrence J. Sejnowski ... 354 pages - Publisher: MIT Press; (2018) ... Language: English - ISBN-10: 9780262038034 - ISBN-13: 978-0262038034.

How deep learning -from Google Translate to driverless cars to personal cognitive assistants- is changing our lives and transforming every sector of the economy. The deep learning revolution has brought us driverless cars, the greatly improved Google Translate, fluent conversations with Siri and Alexa, and enormous profits from automated trading on the New York Stock Exchange. Deep learning networks can play poker better than professional poker players and defeat a world champion at Go. In this book, Terry Sejnowski explains how deep learning went from being an arcane academic field to a disruptive technology in the information economy.

Oliver M. O'Reilly ... 540 pages - Publisher: Cambridge Univ. Press; 2nd edition (March, 2020) ... Language: English - ISBN-10: 1108494218 - ISBN-13: 978-1108494212.

Suitable for both senior-level and first-year graduate courses, this fully revised edition provides a unique and systematic treatment of engineering dynamics that covers Newton-Euler and Lagrangian approaches. New to this edition are: two completely revised chapters on the constraints on, and potential energies for, rigid bodies, and the dynamics of systems of particles and rigid bodies; clearer discussion on coordinate singularities and their relation to mass matrices and configuration manifolds; additional discussion of contravariant basis vectors and dual Euler basis vectors, as well as related works in robotics; improved coverage of navigation equations; inclusion of a 350-page solutions manual for instructors, available online; a fully updated reference list. Numerous structured examples, discussion of various applications, and exercises covering a wide range of topics are included throughout, and source code for exercises, and simulations of systems are available online.

Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong ... 398 pages - Publisher: Cambridge Univ. Press; (April, 2020) ... Language: English - AmazonSIN: B083M7DBP6.

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.

Donald P. Coduto, William A. Kitch, Man-Chu Ronald Yeung ... 984 pages - Publisher: Pearson; 3rd edition (January, 2015) ... Language: English - ISBN-10: 0133411893 - ISBN-13: 978-0133411898.

Jacob Bear ... 592 pages - Publisher: Dover Publications; (January, 2007) ... Language: English - ISBN-10: 0486453553 - ISBN-13: 978-0486453552.

This text explores the laws and equations that govern the flow and storage of groundwater in aquifers. It provides groundwater hydrologists, as well as engineers and planners who deal with the development and management of groundwater resources, with all the necessary tools to forecast the behavior of a regional aquifer system. Following an introduction to the role and management of groundwater in water resource systems, the text examines groundwater balance and motion, mathematical statements of the groundwater forecasting problem, flow in the unsaturated zone, and groundwater quality problems. Additional topics include hydraulics of pumping and recharging wells, fresh and salt water interface in coastal aquifers, modeling of aquifer systems, identification of aquifer parameters, and the use of linear programming in aquifer management. Helpful appendixes and a set of problems corresponding to selected chapters conclude the text.

Shai Shalev-Shwartz, Shai Ben-David ... 415 pages - Publisher: Cambridge Univ. Press; (May, 2014) ... Language: English - AmazonSIN: B00J8LQU8I.

Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics and engineering.

Jeff Heaton ... 375 pages - Publisher: Heaton Research, Inc.; (November, 2015) ... Language: English - AmazonSIN: B0184WRDEQ.

Neural networks have been a mainstay of artificial intelligence since its earliest days. Now, exciting new technologies such as deep learning and convolution are taking neural networks in bold new directions. In this book, we will demonstrate the neural networks in a variety of real-world tasks such as image recognition and data science. We examine current neural network technologies, including ReLU activation, stochastic gradient descent, cross-entropy, regularization, dropout, and visualization.

Kevin P. Murphy ... 1104 pages - Publisher: The MIT Press; (August, 2012) ... Language: English - ISBN-10: 0262018020 - ISBN-13: 978-0262018029.

A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach: Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package―PMTK (probabilistic modeling toolkit)―that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

Allen B. Downey ... 210 pages - Publisher: O'Reilly Media; (October, 2013) ... Language: English - ISBN-10: 1449370780 - ISBN-13: 978-1449370787.

If you know how to program with Python and also know a little about probability, you’re ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical notation, and use discrete probability distributions instead of continuous mathematics. Once you get the math out of the way, the Bayesian fundamentals will become clearer, and you’ll begin to apply these techniques to real-world problems. Bayesian statistical methods are becoming more common and more important, but not many resources are available to help beginners. Based on undergraduate classes taught by author Allen Downey, this book’s computational approach helps you get a solid start. Use your existing programming skills to learn and understand Bayesian statistics + Work with problems involving estimation, prediction, decision analysis, evidence, and hypothesis testing + Get started with simple examples, using coins, M&Ms, Dungeons & Dragons dice, paintball, and hockey + Learn computational methods for solving real-world problems, such as interpreting SAT scores, simulating kidney tumors, and modeling the human microbiome.

Braja M. Das, Nagaratnam Sivakugan ... 944 pages - Publisher: Cengage Learning; 9th edition (December, 2017) ... Language: English - AmazonSIN: B079QG9TJX.

Master the core concepts and applications of foundation analysis and design with Das/Sivakugan’s best-selling Principles of Foundation Engineering 9th (SI) Edition. Written specifically for those studying undergraduate civil engineering, this invaluable resource by renowned authors in the field of geotechnical engineering provides an ideal balance of today's most current research and practical field applications. A wealth of worked-out examples and figures clearly illustrate the work of today's civil engineer, while timely information and insights help readers develop the critical skills needed to properly apply theories and analysis while evaluating soils and foundation design.

Richard Handy, Merlin Spangler ... 904 pages - Publisher: McGraw-Hill Education; 5th edition (January, 2007) ... Language: English - ISBN-10: 0071481206 - ISBN-13: 978-0071481205.

Geotechnical Engineering: Principles and Practices offers students and practicing engineers a concise, easy-to-understand approach to the principles and methods of soil and geotechnical engineering. This updated classic builds from basic principles of soil mechanics and applies them to new topics, including mechanically stabilized earth (MSE), and intermediate foundations. This Fifth Edition features: Over 400 detailed illustrations and photographs + Unique background material on the geological, pedological, and mineralogical aspects of soils with emphasis on clay mineralogy, soil structure, and expansive and collapsible soils. + New coverage of mechanically stabilized earth (MSE); intermediate foundations; in-situ soil testing: statistical analysis of data; “FORE,” a scientific method for analyzing settlement; writing the geotechnical report; and the geotechnical engineer as a sleuth and expert witness.

Get Quick Access to Every Soil and Geotechnical Engineering Topic: • Igneous Rocks as Ultimate Sources for Soils • The Soil Profile • Soil Minerals • Particle Size and Gradation • Soil Fabric and Soil Structure • Soil Density and Unit Weight • Soil Water • Soil Consistency and Engineering Classification • Compaction • Seepage • Stress Distribution • Settlement • Shear Strength • Lateral Stress and Retaining Walls • MSE Walls and Soil Nailing • Slope Stability, Landslides, Embankments, and Earth Dams • Bearing Capacity of Shallow Foundations • Deep Foundations • Intermediate Foundations • Loads on Pipes • In-Situ Testing • Introduction to Soil Dynamics • The Geotechnical Report

Allen B. Downey ... 226 pages - Publisher: O'Reilly Media; 2nd edition (October, 2014) ... Language: English - AmazonSIN: B00OL084UI.

If you know how to program, you have the skills to turn data into knowledge, using tools of probability and statistics. This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python. By working with a single case study throughout this thoroughly revised book, you’ll learn the entire process of exploratory data analysis—from collecting data and generating statistics to identifying patterns and testing hypotheses. You’ll explore distributions, rules of probability, visualization, and many other tools and concepts.

New chapters on regression, time series analysis, survival analysis, and analytic methods will enrich your discoveries. Develop an understanding of probability and statistics by writing and testing code + Run experiments to test statistical behavior, such as generating samples from several distributions + Use simulations to understand concepts that are hard to grasp mathematically + Import data from most sources with Python, rather than rely on data that’s cleaned and formatted for statistics tools + Use statistical inference to answer questions about real-world data.

Nagaratnam Sivakugan, Carthigesu T. Gnanendran, Rabin Tuladhar, M. Bobby ... 512 pages - Publisher: Cengage Learning; (January, 2017) ... Language: English - ASIN: B01N4EZIQC by Amazon.

Readers can now prepare for civil engineering challenges while gaining a broad overview of the materials they will use in their studies and careers with the unique content found in CIVIL ENGINEERING MATERIALS. This invaluable book covers traditional materials, such as concrete, steel, timber, and soils, and also explores non-traditional materials, such as synthetics and industrial-by products. Using numerous practical examples and straight-forward explanations, readers can gain a full understanding of the characteristics and behavior of various materials, how they interact, and how to best utilize and combine traditional and non-traditional materials. In addition to detailing the effective use of civil engineering materials, the book highlights issues related to sustainability to give readers a broader context of how materials are used in contemporary applications.

James Fern, Alexander Rohe, Kenichi Soga, Eduardo Alonso ... 442 pages - Publisher: CRC Press; (January, 2019) ... Language: English - ASIN: B07N8BKJVT by Amazon.

This practical guide provides the best introduction to large deformation material point method (MPM) simulations for geotechnical engineering. It provides the basic theory, discusses the different numerical features used in large deformation simulations, and presents a number of applications -- providing references, examples and guidance when using MPM for practical applications.

MPM covers problems in static and dynamic situations within a common framework. It also opens new frontiers in geotechnical modelling and numerical analysis. It represents a powerful tool for exploring large deformation behaviours of soils, structures and fluids, and their interactions, such as internal and external erosion, and post-liquefaction analysis; for instance the post-failure liquid-like behaviours of landslides, penetration problems such as CPT and pile installation, and scouring problems related to underwater pipelines. In the recent years, MPM has developed enough for its practical use in industry, apart from the increasing interest in the academic world.

Daniel T. Larose  ... 718 pages - Publisher: Freeman/Worth; 2nd edition (January, 2013) ... Language: English - ASIN: B00HQO0UZI by Amazon - ISBN-10: 1464127182 - ISBN-13: 978-1464127182.

Peter Goos, David Meintrup ... 648 pages - Publisher: Wiley; (April, 2016) ... Language: English - ISBN-10: 1119097150 - ISBN-13: 978-1119097150.

This book provides a first course on parameter estimation (point estimates and confidence interval estimates), hypothesis testing, ANOVA and simple linear regression. The authors approach combines mathematical depth with numerous examples and demonstrations using the JMP software. Key features: Provides a comprehensive and rigorous presentation of introductory statistics that has been extensively classroom tested. + Pays attention to the usual parametric hypothesis tests as well as to non-parametric tests (including the calculation of exact p-values). + Discusses the power of various statistical tests, along with examples in JMP to enable in-sight into this difficult topic. + Promotes the use of graphs and confidence intervals in addition to p-values. + Course materials and tutorials for teaching are available on the book's companion website. Masters and advanced students in applied statistics, industrial engineering, business engineering, civil engineering and bio-science engineering will find this book beneficial. It also provides a useful resource for teachers of statistics particularly in the area of engineering.

Ram S. Gupta ... 617 pages - Publisher: CRC Press; 3rd edition (June, 2019) ... Language: English - AmazonSIN: B07TBD778R.

Edward Allen, Joseph Iano ... 944 pages - Publisher: Wiley; 7th edition (September, 2019) ... Language: English - ASIN: B07YCXG7YH by Amazon.

Edward Allen and Joseph Iano’s Fundamentals of Building Construction has been the go-to reference for thousands of professionals and students of architecture, engineering, and construction technology for over thirty years. The materials and methods described in this new Seventh Edition have been thoroughly updated to reflect the latest advancements in the industry. Carefully selected and logically arranged topics—ranging from basic building methods to the principles of structure and enclosure—help readers gain a working knowledge of the field in an enjoyable, easy-to-understand manner. All major construction systems, including light wood frame, mass timber, masonry, steel frame, light gauge steel, and reinforced concrete construction, are addressed. Now in its Seventh Edition, Fundamentals of Building Construction contains substantial revisions and updates. New illustrations and photographs reflect the latest practices and developments in the industry. Revised chapters address exterior wall systems and high-performance buildings, an updated and comprehensive discussion of building enclosure science, evolving tools for assessing environmental and health impacts of building materials, and more. New and exciting developments in mass timber construction are also included.  For the nuts and bolts on building construction practices and materials, Fundamentals of Building Construction: Materials and Methods, 7th Edition lays the foundation that every architect and construction professional needs to build a successful career.

N. Balakrishnan, Markos V. Koutras, Konstadinos G. Politis ... 620 pages - Publisher: Wiley; (April , 2019) ... Language: English - ASIN: B07QGMBC9F by Amazon.

Introduction to Probability offers an authoritative text that presents the main ideas and concepts, as well as the theoretical background, models, and applications of probability. The authors—noted experts in the field—include a review of problems where probabilistic models naturally arise, and discuss the methodology to tackle these problems. A wide-range of topics are covered that include the concepts of probability and conditional probability, univariate discrete distributions, univariate continuous distributions, along with a detailed presentation of the most important probability distributions used in practice, with their main properties and applications.

Designed as a useful guide, the text contains theory of probability, de finitions, charts, examples with solutions, illustrations, self-assessment exercises, computational exercises, problems and a glossary. This important text: • Includes classroom-tested problems and solutions to probability exercises • Highlights real-world exercises designed to make clear the concepts presented • Uses Mathematica software to illustrate the text’s computer exercises • Features applications representing worldwide situations and processes • Offers two types of self-assessment exercises at the end of each chapter, so that students may review the material in that chapter and monitor their progress. Written for students majoring in statistics, engineering, operations research, computer science, physics, and mathematics, Introduction to Probability: Models and Applications is an accessible text that explores the basic concepts of probability and includes detailed information on models and applications.

Aileen Nielsen ... 505 pages - Publisher: O'Reilly Media; (September, 2019) ... Language: English - Amazon SIN: B07Y5WSCV2.

Time series data analysis is increasingly important due to the massive production of such data through the internet of things, the digitalization of healthcare, and the rise of smart cities. As continuous monitoring and data collection become more common, the need for competent time series analysis with both statistical and machine learning techniques will increase. Covering innovations in time series data analysis and use cases from the real world, this practical guide will help you solve the most common data engineering and analysis challengesin time series, using both traditional statistical and modern machine learning techniques. Author Aileen Nielsen offers an accessible, well-rounded introduction to time series in both R and Python that will have data scientists, software engineers, and researchers up and running quickly. You’ll get the guidance you need to confidently: Find and wrangle time series data + Undertake exploratory time series data analysis + Store temporal data + Simulate time series data + Generate and select features for a time series + Measure error + Forecast and classify time series with machine or deep learning + Evaluate accuracy and performance

Richard J. Larsen, Morris L. Marx ... 752 pages - Publisher: Pearson; 6th edition (January, 2017) ... Language: English - ASIN: B076VG8WHV by Amazon.

Introduction to Mathematical Statistics and Its Applications , 6th Edition is a high-level calculus student’s first exposure to mathematical statistics. This book provides students who have already taken three or more semesters of calculus with the background to apply statistical principles. Meaty enough to guide a two-semester course, the book touches on both statistics and experimental design, which teaches students various ways to analyze data. It gives computational-minded students a necessary and realistic exposure to identifying data models.

Allen Downey ... 200 pages - Publisher: O'Reilly Media; 2nd edition (July, 2018) ... Language: English - ASIN: B07FFC87K8 by Amazon.

Complexity science uses computation to explore the physical and social sciences. In Think Complexity, you’ll use graphs, cellular automata, and agent-based models to study topics in physics, biology, and economics.Whether you’re an intermediate-level Python programmer or a student of computational modeling, you’ll delve into examples of complex systems through a series of worked examples, exercises, case studies, and easy-to-understand explanations. In this updated second edition, you will: Work with NumPy arrays and SciPy methods, including basic signal processing and Fast Fourier Transform + Study abstract models of complex physical systems, including power laws, fractals and pink noise, and Turing machines + Get Jupyter notebooks filled with starter code and solutions to help you re-implement and extend original experiments in complexity; and models of computation like Turmites, Turing machines, and cellular automata + Explore the philosophy of science, including the nature of scientific laws, theory choice, and realism and instrumentalism. Ideal as a text for a course on computational modeling in Python, Think Complexity also helps self-learners gain valuable experience with topics and ideas they might not encounter otherwise.

Roxy Peck, Tom Short ... 729 pages - Publisher: Cengage Learning; 2nd edition (January, 2018) ... Language: English - ISBN-10: 1337558087 - ISBN-13: 978-1337558082.

Statistics: Learning from Data 2nd Edition helps you learn to think like a statistician. It pays particular attention to areas that students often struggle with -- probability, hypothesis testing, and selecting an appropriate method of analysis. Supported by learning objectives, real-data examples and exercises, and technology notes, this book helps you to develop conceptual understanding, mechanical proficiency, and the ability to put knowledge into practice.

Steve McKillup ... 420 pages - Publisher: Cambridge Univ. Press; 2nd edition (November, 2011) ... Language: English - ASIN: B0072J3KAO by Amazon.

An understanding of statistics and experimental design is essential for life science studies, but many students lack a mathematical background and some even dread taking an introductory statistics course. Using a refreshingly clear and encouraging reader-friendly approach, this book helps students understand how to choose, carry out, interpret and report the results of complex statistical analyses, critically evaluate the design of experiments and proceed to more advanced material. Taking a straightforward conceptual approach, it is specifically designed to foster understanding, demystify difficult concepts and encourage the unsure. Even complex topics are explained clearly, using a pictorial approach with a minimum of formulae and terminology. Examples of tests included throughout are kept simple by using small data sets. In addition, end-of-chapter exercises, new to this edition, allow self-testing. Handy diagnostic tables help students choose the right test for their work and remain a useful refresher tool for postgraduates.

Sorin Draghici ... 1036 pages - Publisher: Chapman and Hall/CRC; 2nd edition (April, 2016) ... Language: English - ASIN: B00O5D331Q by Amazon.

Richly illustrated in color, Statistics and Data Analysis for Microarrays Using R and Bioconductor, Second Edition provides a clear and rigorous description of powerful analysis techniques and algorithms for mining and interpreting biological information. Omitting tedious details, heavy formalisms, and cryptic notations, the text takes a hands-on, example-based approach that teaches students the basics of R and microarray technology as well as how to choose and apply the proper data analysis tool to specific problems.

New to the Second Edition: Completely updated and double the size of its predecessor, this timely second edition replaces the commercial software with the open source R and Bioconductor environments. Fourteen new chapters cover such topics as the basic mechanisms of the cell, reliability and reproducibility issues in DNA microarrays, basic statistics and linear models in R, experiment design, multiple comparisons, quality control, data pre-processing and normalization, Gene Ontology analysis, pathway analysis, and machine learning techniques. Methods are illustrated with toy examples and real data and the R code for all routines is available on an accompanying CD-ROM. With all the necessary prerequisites included, this best-selling book guides students from very basic notions to advanced analysis techniques in R and Bioconductor. The first half of the text presents an overview of microarrays and the statistical elements that form the building blocks of any data analysis. The second half introduces the techniques most commonly used in the analysis of microarray data.

John MacInnes ... 334 pages - Publisher: SAGE Publications Ltd; (December, 2016) ... Language: English - ASIN: B01JZ7IRCG by Amazon.

Many professional, high-quality surveys collect data on people's behaviour, experiences, lifestyles and attitudes. The data they produce is more accessible than ever before. This book provides students with a comprehensive introduction to using this data, as well as transactional data and big data sources, in their own research projects. Here you will find all you need to know about locating, accessing, preparing and analysing secondary data, along with step-by-step instructions for using IBM SPSS Statistics.

You will learn how to: Create a robust research question and design that suits secondary analysis + Locate, access and explore data online + Understand data documentation + Check and 'clean' secondary data + Manage and analyse your data to produce meaningful results + Replicate analyses of data in published articles and books. Using case studies and video animations to illustrate each step of your research, this book provides you with the quantitative analysis skills you'll need to pass your course, complete your research project and compete in the job market. Exercises throughout the book and on the book's companion website give you an opportunity to practice, check your understanding and work hands on with real data as you're learning.

Robert W. Christopherson, Ginger Birkeland ... 688 pages - Publisher: Pearson; 10th edition (February, 2017) ... Language: English - ASIN: B06WVT65WP by Amazon.

The most authoritative introduction to physical geography with new applications to real-world geography: Among the most highly regarded texts in physical geography, Robert Christopherson’s best-selling Geosystems: An Introduction to Physical Geography is known for its meticulous attention to detail, up-to-date accuracy, and rich integration of climate change science. Uniquely organized, the text presents Earth systems topics as they naturally occur: atmosphere, hydrosphere, lithosphere, and biosphere. This interconnected and organic systems-based approach is highlighted in strong pedagogical tools, a structured learning path, and current, updated information. The 10th Edition includes new features and activities that help readers apply geography to the real world, revised infographics, and new exercises designed to enhance learning.

Laura Igual, Santi Segui ... 218 pages - Publisher: Springer; (March, 2017) ... Language: English - ISBN-10: 3319500163 - ISBN-13: 978-331950016.

This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the emerging and interdisciplinary field of data science. The coverage spans key concepts adopted from statistics and machine learning, useful techniques for graph analysis and parallel programming, and the practical application of data science for such tasks as building recommender systems or performing sentiment analysis. Topics and features: provides numerous practical case studies using real-world data throughout the book; supports understanding through hands-on experience of solving data science problems using Python; describes techniques and tools for statistical analysis, machine learning, graph analysis, and parallel programming; reviews a range of applications of data science, including recommender systems and sentiment analysis of text data; provides supplementary code resources and data at an associated website.

John M. Stewart ... 230 pages - Publisher: Cambridge University Press; (August, 2014) ... Language: English - ISBN-10: 1107686423 - ISBN-13: 978-1107686427.

Python is a free, open source, easy-to-use software tool that offers a significant alternative to proprietary packages such as MATLAB and Mathematica. This book covers everything the working scientist needs to know to start using Python effectively. The author explains scientific Python from scratch, showing how easy it is to implement and test non-trivial mathematical algorithms and guiding the reader through the many freely available add-on modules. A range of examples, relevant to many different fields, illustrate the program's capabilities. In particular, readers are shown how to use pre-existing legacy code (usually in Fortran77) within the Python environment, thus avoiding the need to master the original code. Instead of exercises the book contains useful snippets of tested code which the reader can adapt to handle problems in their own field, allowing students and researchers with little computer expertise to get up and running as soon as possible.

Oliver R. Simpson ... 160 pages - Publisher: UnKnown; (November, 2019) ... Language: English - ASIN: B081JBHPLV by Amazon.

If your dream is to become a Python pro, or you always want to code like a boss, solve real-world problems, build web apps, or even automate repetitive tasks, then you’ve come to the right place. By the end of this book, you will have mastered the fundamentals of Python language. You will learn how to create programs that will save you time and simplify your life. Python is a straightforward language, so even if this is your first time to learn any programming language, you can learn Python without experiencing any issues. Python has multiple applications, and so you've got a high probability of getting a great job once you become a pro in Python language.

Whether it’s in web development, data analysis, scripting, and machine learning, all require that you be experienced in Python. This is a beginner guide that will take you from absolute scratch to creating your first Python program. Not only that, but you’ll also learn lots of stuff about the Django Python framework. With Django, you will learn how to create web apps very quickly and efficiently.

J. Burton Browning, Marty Alchin ... 384 pages - Publisher: Apress; 2nd edition (December, 2014) ... Language: English - ISBN-10: 1484203356 - ISBN-13: 978-1484203354.

You’ve learned the basics of Python, but how do you take your skills to the next stage? Even if you know enough to be productive, there are a number of features that can take you to the next level in Python. Pro Python, Second Edition explores concepts and features normally left to experimentation, allowing you to be even more productive and creative. In addition to pure code concerns, Pro Python develops your programming techniques and approaches, which will help make you a better Python programmer. This book will improve not only your code but also your understanding and interaction with the many established Python communities. This book takes your Python knowledge and coding skills to the next level. It shows you how to write clean, innovative code that will be respected by your peers. With this book, make your code do more with introspection and meta-programming. And learn and later use the nuts and bolts of an application, tier-by-tier as a complex case study along the way.

What you’ll learn: Write strong Python code that will be respected in the Python community + Understand the reasons behind big design decisions in Python + Write programs that can reconfigure themselves in Python + Disguise your code as different types of objects in Python + Inspect just about any object in Python + Prepare your code for international audiences + Ensure code quality with rigorous testing. Who this book is for: This book is for intermediate to advanced Python programmers who are looking to understand how and why Python works the way it does and how they can take their code to the next level.

Jonathan Yates ... 170 pages - Publisher: UnKnown; (July, 2016) ... Language: English - ASIN: B01I5P72PC by Amazon.

Are you aware that websites like Instagram, Spotify, and Pinterest use Python Programming in to make their sites? Will you create the next Instagram with your newfound expertise in Python? Python Programming is a widely used language that anyone can use and get good with, and also a super concise language that you can create nearly anything with. Mac, Linux, UNIX, and others have Python installed as a default setting since it is an open source and free language. After you read this book, you will fluent in this versatile code language and see it applied to a variety of examples now with pictures! As stated, you can use the language to create everything you want; a website, make a game, or even create a search engine. The big plus of using Python is, an explicit compiler is not necessary since it’s an entirely interpreted language like Perl, Shell, and others.