the Academic NEST

Kumar Molugaram, G. Shanker Rao ... 538 pages - Publisher: Butterworth-Heinemann; (March, 2017) ... Language: English - AmazonSIN: B06XFRF985.

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.