the Academic NEST

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.