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Noah Gift ... 272 pages - Publisher: Addison-Wesley Professional; (September, 2018) ... Language: English - ISBN-10: 0134863860 - ISBN-13: 978-0134863863.

Master Powerful Off-the-Shelf Business Solutions for AI and Machine Learning: Pragmatic AI will help you solve real-world problems with contemporary machine learning, artificial intelligence, and cloud computing tools. Noah Gift demystifies all the concepts and tools you need to get results—even if you don’t have a strong background in math or data science. Gift illuminates powerful off-the-shelf cloud offerings from Amazon, Google, and Microsoft, and demonstrates proven techniques using the Python data science ecosystem. His workflows and examples help you streamline and simplify every step, from deployment to production, and build exceptionally scalable solutions. As you learn how machine language (ML) solutions work, you’ll gain a more intuitive understanding of what you can achieve with them and how to maximize their value.

Building on these fundamentals, you’ll walk step-by-step through building cloud-based AI/ML applications to address realistic issues in sports marketing, project management, product pricing, real estate, and beyond. Whether you’re a business professional, decision-maker, student, or programmer, Gift’s expert guidance and wide-ranging case studies will prepare you to solve data science problems in virtually any environment. Get and configure all the tools you’ll need + Quickly review all the Python you need to start building machine learning applications + Master the AI and ML tool chain and project lifecycle + Work with Python data science tools such as IPython, Pandas, Numpy, Juypter Notebook, and Sklearn + Incorporate a pragmatic feedback loop that continually improves the efficiency of your workflows and systems + Develop cloud AI solutions with Google Cloud Platform, including TPU, Colaboratory, and Datalab services + Define Amazon Web Services cloud AI workflows, including spot instances, code pipelines, boto, and more + Work with Microsoft Azure AI APIs + Walk through building six real-world AI applications, from start to finish.

Charu C. Aggarwal ... 520 pages - Language: ‎English - Publisher: Springer; (September, 2018) - ISBN-10:‎ 3319944622 - ISBN-13:‎ 978-3319944623.

This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. Applications associated with many different areas like recommender systems, machine translation, image captioning, image classification, reinforcement-learning based gaming, and text analytics are covered. The chapters of this book span three categories:

The basics of neural networks: Many traditional machine learning models can be understood as special cases of neural networks. An emphasis is placed in the first two chapters on understanding the relationship between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. These methods are studied together with recent feature engineering methods like word2vec. Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 3 and 4. Chapters 5 and 6 present radial-basis function (RBF) networks and restricted Boltzmann machines. Advanced topics in neural networks: Chapters 7 and 8 discuss recurrent neural networks and convolutional neural networks. Several advanced topics like deep reinforcement learning, neural Turing machines, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 9 and 10.

The book is written for graduate students, researchers, and practitioners. Numerous exercises are available along with a solution manual to aid in classroom teaching. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.

Peter Sestoft ... 341 pages - Publisher: Springer; 2nd edition (September, 2017) ... Language: English - ISBN-10: 331960788X - ISBN-13: 978-3319607887.

This book uses a functional programming language (F#) as a metalanguage to present all concepts and examples, and thus has an operational flavour, enabling practical experiments and exercises. It includes basic concepts such as abstract syntax, interpretation, stack machines, compilation, type checking, garbage collection, and real machine code. Also included are more advanced topics on polymorphic types, type inference using unification, co- and contravariant types, continuations, and backwards code generation with on-the-fly peephole optimization. This second edition includes two new chapters. One describes compilation and type checking of a full functional language, tying together the previous chapters. The other describes how to compile a C subset to real (x86) hardware, as a smooth extension of the previously presented compilers.The examples present several interpreters and compilers for toy languages, including compilers for a small but usable subset of C, abstract machines, a garbage collector, and ML-style polymorphic type inference. Each chapter has exercises. Programming Language Concepts covers practical construction of lexers and parsers, but not regular expressions, automata and grammars, which are well covered already. It discusses the design and technology of Java and C# to strengthen students’ understanding of these widely used languages.

Allan Ludman, Stephen Marshak ... 528 pages - Publisher: W.W. Norton; 4th edition (July, 2019) ... Language: English - ISBN-13: 978-0393617528.

Engaging, hands-on, and visual―the geology manual that helps your students think like a geologist: The Third Edition has been thoroughly updated to help make your geology lab more active and engaging. This edition features new “What Do You Think” mini-cases that promote critical thinking, new and vastly-improved topographic maps, and updated, detailed reference figures in every chapter. With low prices and package deals available with all Marshak texts, the Laboratory Manual for Introductory Geology, Third Edition, is truly the best choice for your lab.

Jasbir Singh Arora ... 912 pages - Publisher: Academic Press; 4th edition (April, 2016) - Language: English - ISBN-10: 0128008067 - ISBN-13: 978-0128008065.

Introduction to Optimum Design, Fourth Edition, carries on the tradition of the most widely used textbook in engineering optimization and optimum design courses. It is intended for use in a first course on engineering design and optimization at the undergraduate or graduate level in engineering departments of all disciplines, with a primary focus on mechanical, aerospace, and civil engineering courses. Through a basic and organized approach, the text describes engineering design optimization in a rigorous, yet simplified manner, illustrates various concepts and procedures with simple examples, and demonstrates their applicability to engineering design problems.

Formulation of a design problem as an optimization problem is emphasized and illustrated throughout the text using Excel and MATLAB as learning and teaching aids. This fourth edition has been reorganized, rewritten in parts, and enhanced with new material, making the book even more appealing to instructors regardless of course level. Includes basic concepts of optimality conditions and numerical methods that are described with simple and practical examples, making the material highly teachable and learnable + Presents applications of optimization methods for structural, mechanical, aerospace, and industrial engineering problems + Provides practical design examples that introduce students to the use of optimization methods early in the book + Contains chapter on several advanced optimum design topics that serve the needs of instructors who teach more advanced courses.

Dimitrios Kolymbas ... 199 pages - Language: English - Publisher: Cambridge Univ. Press; (August, 2022).


A Primer to Theoretical Soil Mechanics is about adapting continuum mechanics to granular materials. The field of continuum mechanics offers many fruitful concepts and methods, however there is declining interest in the field due to its complex and fragmented nature. This book's purpose is therefore to facilitate the understanding of the theoretical principles of soil mechanics, as well as introducing the new theory of barodesy. This title argues for barodesy as a simple alternative to the plasticity theory used currently and provides a systematic insight into this new constitutive model for granular materials. This book therefore introduces a complex field from a fresh and innovative perspective using simple concepts, succinct equations and explanatory sketches. Intended for advanced undergraduates, graduates and PhD students, this title is also apt for researchers seeking advanced training on fundamental topics.

P. Purushothama Raj ... 880 pages - Publisher: Pearson; 2nd edition (July, 2013) ... Language: English - AmazonSIN: B00I962Z3C.

Soil Mechanics and Foundation Engineering, 2nd edition Presents the principles of soil mechanics and foundation engineering in a simplified yet logical manner that assumes no prior knowledge of the subject. It includes all the relevant content required for a sound background in the subject, reinforcing theoretical aspects with comprehensive practical applications. Contents: chapter 1: soil formation and composition chapter 2: index properties of soils chapter 3: identification and classification of soils chapter 4: compaction of soils chapter 5: permeability and capillarity chapter 6: seepage chapter 7: stress and stress distribution in soil chapter 8: consolidation and consolidation settlement chapter 9: shear strength of soils chapter 10: laboratory measurement of soil properties chapter 11: lateral earth pressure chapter 12: earth- retaining structures chapter 13: stability of slopes chapter 14: bearing capacity of soils chapter 15: shallow foundations chapter 16: pile foundation chapter 17: drilled piers and caisson foundations chapter 18: ground investigation chapter 19: soil improvement chapter 20: embankment dams chapter 21: dynamic loading of soil chapter 22: environmental geotechnology chapter 23: introductory rock mechanics chapter 24: pavements.

Amir R. Khoei ... 584 pages - Publisher: Wiley; (February, 2015) ... Language: English - ISBN-10: 1118457684 - ISBN-13: 978-1118457689.

The finite element method (FEM) technique has been developed to simulate and analyze complex engineering problems. However, there are a number of drawbacks with finite element simulation of discontinuous problems, such as fracture mechanics problems, including the computational cost of a very fine finite element mesh and the complex remeshing strategy in capturing discontinuity. The extended finite element method (XFEM) has therefore been developed to improve the performance of the conventional finite element method in discontinuity problems. Extended Finite Element Method: Theory and Applications introduces the theory and applications of XFEM in the linear and nonlinear problems of continua, structures, and geomechanics. It begins by introducing the concept of a partition of unity, various enrichment functions, and fundamentals of XFEM formulation. It then covers the theory and application of XFEM in large deformations, plasticity, and contact problems. The implementation of XFEM in fracture mechanics, including linear, cohesive, and ductile crack propagation, is also covered. Finally, the theory and applications of XFEM in multiphase fluid flow, including hydraulic fracturing in soil saturated media and crack propagation in thermo-hydro-mechanical porous media, are discussed in detail. Key features: Comprehensively introduces XFEM analysis + Explains the theory and applications of XFEM in various continuum and geo-mechanical problems + Includes worked examples + Accompanied by a website hosting source code and examples. Extended Finite Element Method: Theory and Applications is a comprehensive introduction to XFEM analysis for researchers and practitioners in industry, and is also an ideal textbook for graduate students in mechanical and civil engineering.ccompanied by a website hosting source code and examples

Mohammad Karamouz ... 924 pages - Language: English - Publisher: CRC Press; (December, 2021) - AmazonSIN: ‎B09LR3V8QJ.

This book presents three distinct pillars for analysis, design, and planning: urban water cycle and variability as the state of water being; landscape architecture as the medium for built-by-design; and total systems as the planning approach. The increasing demand for water and urban and industrial expansions have caused myriad environmental, social, economic, and political predicaments. More frequent and severe floods and droughts have changed the resiliency and ability of water infrastructure systems to operate and provide services to the public. These concerns and issues have also changed the way we plan and manage our water resources. Focusing on urban challenges and contexts, the book provides foundational information regarding water science and engineering while also examining topics relating to urban stormwater, water supply, and wastewater infrastructures. It also addresses critical emerging issues such as simulation and economic modeling, flood resiliency, environmental visualization, satellite data applications, and digital data model (DEM) advancements.

Features: Explores various theoretical, practical, and real-world applications of system analysis, design, and planning of urban water infrastructures + Discusses hydrology, hydraulics, and basic laws of water flow movement through natural and constructed environments + Describes a wide range of novel topics ranging from water assets, water economics, systems analysis, risk, reliability, and disaster management + Examines the details of hydrologic and hydrodynamic modeling and simulation of conceptual and data-driven models + Delineates flood resiliency, environmental visualization, pattern recognition, and machine learning attributes + Explores a compilation of tools and emerging techniques that elevate the reader to a higher plateau in water and environmental systems management.

Water Systems Analysis, Design, and Planning: Urban Infrastructure serves as a useful resource for advanced undergraduate and graduate students taking courses in the areas of water resources and systems analysis, as well as practicing engineers and landscape professionals.

Igor Griva, Stephen G. Nash, Ariela Sofer ... 764 pages - Publisher: Society for Industrial Mathematics; 2nd edition (December, 2008) ... Language: English - ISBN-10: 0898716616 - ISBN-13: 978-0898716610.

This book introduces the applications, theory, and algorithms of linear and nonlinear optimization, with an emphasis on the practical aspects of the material. Its unique modular structure provides flexibility to accommodate the varying needs of instructors, students, and practitioners with different levels of sophistication in these topics. The succinct style of this second edition is punctuated with numerous real-life examples and exercises, and the authors include accessible explanations of topics that are not often mentioned in textbooks, such as duality in nonlinear optimization, primal-dual methods for nonlinear optimization, filter methods, and applications such as support-vector machines. Part I of Linear and Nonlinear Optimization, Second Edition provides fundamentals that can be taught in whole or in part at the beginning of a course on either topic and then referred to as needed. Part II on linear programming and Part III on unconstrained optimization can be used together or separately, and Part IV on nonlinear optimization can be taught without having studied the material in Part II. In the preface the authors suggest course outlines that can be adjusted to the requirements of a particular course on both linear and nonlinear optimization, or to separate courses on these topics. Three appendices provide information on linear algebra, other fundamentals, and software packages for optimization problems. A supplemental website offers auxiliary data sets that are necessary for some of the exercises.

Audience: This book is primarily intended for use in linear and nonlinear optimization courses for advanced undergraduate and graduate students. It is also appropriate as a tutorial for researchers and practitioners who need to understand the modern algorithms of linear and nonlinear optimization to apply them to problems in science and engineering. Contents: Preface; Part I: Basics; Chapter 1: Optimization Models; Chapter 2: Fundamentals of Optimization; Chapter 3: Representation of Linear Constraints; Part II: Linear Programming; Chapter 4: Geometry of Linear Programming; Chapter 5: The Simplex Method; Chapter 6: Duality and Sensitivity; Chapter 7: Enhancements of the Simplex Method; Chapter 8: Network Problems; Chapter 9: Computational Complexity of Linear Programming; Chapter 10: Interior-Point Methods of Linear Programming; Part III: Unconstrained Optimization; Chapter 11: Basics of Unconstrained Optimization; Chapter 12: Methods for Unconstrained Optimization; Chapter 13: Low-Storage Methods for Unconstrained Problems; Part IV: Nonlinear Optimization; Chapter 14: Optimality Conditions for Constrained Problems; Chapter 15: Feasible-Point Methods; Chapter 16: Penalty and Barrier Methods; Part V: Appendices; Appendix A: Topics from Linear Algebra; Appendix B: Other Fundamentals; Appendix C: Software; Bibliography; Index

Wenyu Sun, Ya-Xiang Yuan ... 688 pages - Publisher: Springer; (May, 2006) ... Language: English - ISBN-10: 0387249753 - ISBN-13: 978-0387249759.

This book, a result of the author's teaching and research experience in various universities and institutes over the past ten years, can be used as a textbook for an optimization course for graduates and senior undergraduates. It systematically describes optimization theory and several powerful methods, including recent results. For most methods, the authors discuss an idea’s motivation, study the derivation, establish the global and local convergence, describe algorithmic steps, and discuss the numerical performance. The book deals with both theory and algorithms of optimization concurrently. It also contains an extensive bibliography. Finally, apart from its use for teaching, Optimization Theory and Methods will be very beneficial as a research reference.

Dusan Teodorovic, Milan Janic ... 900 pages - Publisher: Butterworth-Heinemann; (October, 2016) - Language: ‎English - ISBN-10: 0128038187 - ISBN-13: 978-0128038185.


Transportation Engineering: Theory, Practice and Modeling is a guide for integrating multi-modal transportation networks and assessing their potential cost and impact on society and the environment. Clear and rigorous in its coverage, the authors begin with an exposition of theory related to traffic engineering and control, transportation planning, and an evaluation of transportation alternatives that is followed by models and methods for predicting travel and freight transportation demand, analyzing existing and planning new transportation networks, and developing traffic control tactics and strategies. Written by an author team with over thirty years of experience in both research and teaching, the book incorporates both theory and practice to facilitate greener solutions.

Richard W. Hamming ... Publisher: ‎Dover Publications; (November, 2004) - Language:‎ English - 880 pages - ISBN-10:‎ 0486439453 - ISBN-13:‎ 978-0486439457.


Understanding calculus is vital to the creative applications of mathematics in numerous areas. This text focuses on the most widely used applications of mathematical methods, including those related to other important fields such as probability and statistics. The four-part treatment begins with algebra and analytic geometry and proceeds to an exploration of the calculus of algebraic functions and transcendental functions and applications. In addition to three helpful appendixes, the text features answers to some of the exercises. Appropriate for advanced undergraduates and graduate students, it is also a practical reference for professionals. 1985 edition. 310 figures. 18 tables.

Jon Krohn, Grant Beyleveld, Aglaé Bassens ... Publisher: ‎ Addison-Wesley Professional; (September, 2019) - Language: ‎ English - 416 pages - ISBN-10: ‎ 0135116694 - ISBN-13: ‎ 978-0135116692.

Deep learning is one of today’s hottest fields. This approach to machine learning is achieving breakthrough results in some of today’s highest profile applications, in organizations ranging from Google to Tesla, Facebook to Apple. Thousands of technical professionals and students want to start leveraging its power, but previous books on deep learning have often been non-intuitive, inaccessible, and dry. In Deep Learning Illustrated, three world-class instructors and practitioners present a uniquely visual, intuitive, and accessible high-level introduction to the techniques and applications of deep learning. Packed with vibrant, full-color illustrations, it abstracts away much of the complexity of building deep learning models, making the field more fun to learn, and accessible to a far wider audience.

Part I’s high-level overview explains what Deep Learning is, why it has become so ubiquitous, and how it relates to concepts and terminology such as Artificial Intelligence, Machine Learning, Artificial Neural Networks, and Reinforcement Learning. These opening chapters are replete with vivid illustrations, easy-to-grasp analogies, and character-focused narratives. Building on this foundation, the authors then offer a practical reference and tutorial for applying a wide spectrum of proven deep learning techniques. Essential theory is covered with as little mathematics as possible, and illuminated with hands-on Python code. Theory is supported with practical “run-throughs” available in accompanying Jupyter notebooks, delivering a pragmatic understanding of all major deep learning approaches and their applications: machine vision, natural language processing, image generation, and videogaming. To help readers accomplish more in less time, the authors feature several of today’s most widely-used and innovative deep learning libraries, including TensorFlow and its high-level API, Keras; PyTorch, and the recently-released high-level Coach, a TensorFlow API that abstracts away the complexity typically associated with building Deep Reinforcement Learning algorithms.

Ann Lehman, Norm O'Rourke, Larry Hatcher, Edward J. Stepanski ... 481 pages - Language: ‎ English - Publisher: ‎ SAS Institute; (February, 2005) - ISBN-10: ‎1590475763 - ISBN-13: 978-1590475768.


Doing statistics in JMP has never been easier! Learn how to manage JMP data and perform the statistical analyses most commonly used in research in the social sciences and other fields with JMP for Basic Univariate and Multivariate Statistics: A Step-by-Step Guide, authored by Ann Lehman, Norm O'Rourke, Larry Hatcher, and Edward Stepanski. Clearly written instructions guide you through the basic concepts of research and data analysis, enabling you to easily perform statistical analyses and solve problems in real-world research. Step by step, you'll discover how to obtain descriptive and inferential statistics, summarize results, perform a wide range of JMP analyses, interpret the results, and more. Topics include: screening data for errors and selecting subsets with the JMP Distribution platform, computing the coefficient alpha reliability index (Cronbach's alpha) for a multiple-item scale, performing bivariate analyses for all types of variables, performing a one-way analysis of variance (ANOVA), performing a multiple regression, and using the JMP Fit Model platform to perform a one-way multivariate analysis of variance (MANOVA). This user-friendly book introduces researchers and students of the social sciences to JMP and to elementary statistical procedures, while more advanced statistical procedures that are presented make it an invaluable reference guide for experienced researchers as well.

Cameron Davidson-Pilon  ... 256 pages - Publisher: ‎Addison-Wesley Professional; (October, 2015) - Language: English -  ISBN-10: 0133902838 - ISBN-13: 978-0133902839.

Bayesian methods of inference are deeply natural and extremely powerful. However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice–freeing you to get results using computing power. Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention.

Davidson-Pilon begins by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, he introduces PyMC through a series of detailed examples and intuitive explanations that have been refined after extensive user feedback. You’ll learn how to use the Markov Chain Monte Carlo algorithm, choose appropriate sample sizes and priors, work with loss functions, and apply Bayesian inference in domains ranging from finance to marketing. Once you’ve mastered these techniques, you’ll constantly turn to this guide for the working PyMC code you need to jumpstart future projects.

Coverage includes: • Learning the Bayesian “state of mind” and its practical implications • Understanding how computers perform Bayesian inference • Using the PyMC Python library to program Bayesian analyses • Building and debugging models with PyMC • Testing your model’s “goodness of fit” • Opening the “black box” of the Markov Chain Monte Carlo algorithm to see how and why it works • Leveraging the power of the “Law of Large Numbers” • Mastering key concepts, such as clustering, convergence, autocorrelation, and thinning • Using loss functions to measure an estimate’s weaknesses based on your goals and desired outcomes • Selecting appropriate priors and understanding how their influence changes with dataset size • Overcoming the “exploration versus exploitation” dilemma: deciding when “pretty good” is good enough • Using Bayesian inference to improve A/B testing • Solving data science problems when only small amounts of data are available.

Randall Pruim ... 820 pages - Publisher: American Mathematical Society; 2nd Edition (April, 2018) - Language: English - ISBN-10: ‎1470428482 - ISBN-13: 978-1470428488.

Foundations and Applications of Statistics simultaneously emphasizes both the foundational and the computational aspects of modern statistics. Engaging and accessible, this book is useful to undergraduate students with a wide range of backgrounds and career goals. The exposition immediately begins with statistics, presenting concepts and results from probability along the way. Hypothesis testing is introduced very early, and the motivation for several probability distributions comes from p-value computations. Pruim develops the students' practical statistical reasoning through explicit examples and through numerical and graphical summaries of data that allow intuitive inferences before introducing the formal machinery. The topics have been selected to reflect the current practice in statistics, where computation is an indispensible tool. In this vein, the statistical computing environment $\textsf{R}$ is used throughout the text and is integral to the exposition. Attention is paid to developing students' mathematical and computational skills as well as their statistical reasoning. Linear models, such as regression and ANOVA, are treated with explicit reference to the underlying linear algebra, which is motivated geometrically. Foundations and Applications of Statistics discusses both the mathematical theory underlying statistics and practical applications that make it a powerful tool across disciplines. The book contains ample material for a two-semester course in undergraduate probability and statistics. A one-semester course based on the book will cover hypothesis testing and confidence intervals for the most common situations.

Subal C. Kumbhakar, Hung-Jen Wang, Alan P. Horncastle ... 374 pages - Language: ‎ English - Publisher: ‎ Cambridge Univ. Press; (January, 2015) - ISBN-10: ‎ 1107609461 - ISBN-13: 978-1107609464.


Stochastic Frontier Analysis Using Stata provides practitioners in academia and industry with a step-by-step guide on how to conduct efficiency analysis using the stochastic frontier approach. The authors explain in detail how to estimate production, cost, and profit efficiency and introduce the basic theory of each model in an accessible way, using empirical examples that demonstrate the interpretation and application of models. This book also provides computer code, allowing users to apply the models in their own work, and incorporates the most recent stochastic frontier models developed in academic literature. Such recent developments include models of heteroscedasticity and exogenous determinants of inefficiency, scaling models, panel models with time-varying inefficiency, growth models, and panel models that separate firm effects and persistent and transient inefficiency. Immensely helpful to applied researchers, this book bridges the chasm between theory and practice, expanding the range of applications in which production frontier analysis may be implemented.

Rémy Lentzner ... 111 pages - Language: ‎English - Publisher: Remylent; (April, 2021).

Excel makes life easier because its powerful functions are easily accessible. With the spreadsheet you can handle numbers, dates, formulas or simply text. This book will be an asset to improve your skills by learning more complex functions and finally to save a lot of time. The examples presented in this manual will help you to analyse data better. Do not hesitate to test them in order to discover excel's strengths. It reflects real management situations. You can reproduce them with all Excel versions. Accessible to all, this manual guides you, step by step, to increase your knowledge.

Russell Hibbeler ... 923 pages - Language: English - ISBN-10: 1292247304 - ISBN-13: 978-1292247304.

For Fluid Mechanics courses found in Civil and Environmental, General Engineering, and Engineering Technology and Industrial Management departments.Fluid Mechanics is intended to provide a comprehensive guide to a full understanding of the theory and many applications of fluid mechanics. The text features many of the hallmark pedagogical aids unique to Hibbeler texts, including its student-friendly, clear organization. The text supports the development of student problem-solving skills through a large variety of problems, representing a broad range of engineering disciplines that stress practical, realistic situations encountered in professional practice, and provide varying levels of difficulty. The text offers flexibility in that basic principles are covered in chapters 1-6, and the remaining chapters can be covered in any sequence without the loss of continuity.Updates to the 2nd Edition result from comments and suggestions from colleagues, reviewers in the teaching profession, and many of the author’s students, and include expanded topic coverage and new Example and Fundamental Problems intended to further students’ understanding of the theory and its applications.Pearson Mastering™ Engineering is not included. Students, if Pearson Mastering Engineering is a recommended/mandatory component of the course, please ask your instructor for the correct ISBN. Mastering Engineering should only be purchased when required by an instructor. Instructors, contact your Pearson representative for more information.Reach every student by pairing this text with Pearson Mastering EngineeringMastering is the teaching and learning platform that empowers you to reach every student. By combining trusted author content with digital tools and a flexible platform, Mastering personalizes the learning experience and improves results for each student.

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