Download or read book An Introduction to Deep Reinforcement Learning written by Vincent Francois-Lavet and published by Foundations and Trends (R) in Machine Learning. This book was released on 2018-12-20 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has recently been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine. Deep RL opens up many new applications in domains such as healthcare, robotics, smart grids, finance, and many more. This book provides the reader with a starting point for understanding the topic. Although written at a research level it provides a comprehensive and accessible introduction to deep reinforcement learning models, algorithms and techniques. Particular focus is on the aspects related to generalization and how deep RL can be used for practical applications. Written by recognized experts, this book is an important introduction to Deep Reinforcement Learning for practitioners, researchers and students alike.

Download or read book Soil Dynamics and Foundation Modeling written by Junbo Jia and published by Springer. This book was released on 2017-11-26 with total page 741 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a comprehensive topical overview on soil dynamics and foundation modeling in offshore and earthquake engineering. The spectrum of topics include, but is not limited to, soil behavior, soil dynamics, earthquake site response analysis, soil liquefactions, as well as the modeling and assessment of shallow and deep foundations. The author provides the reader with both theory and practical applications, and thoroughly links the methodological approaches with engineering applications. The book also contains cutting-edge developments in offshore foundation engineering such as anchor piles, suction piles, pile torsion modeling, soil ageing effects and scour estimation. The target audience primarily comprises research experts and practitioners in the field of offshore engineering, but the book may also be beneficial for graduate students.

Download or read book Analysis and Design of Shallow and Deep Foundations written by Lymon C. Reese and published by John Wiley & Sons. This book was released on 2005-11-25 with total page 608 pages. Available in PDF, EPUB and Kindle. Book excerpt: One-of-a-kind coverage on the fundamentals of foundation analysis and design Analysis and Design of Shallow and Deep Foundations is a significant new resource to the engineering principles used in the analysis and design of both shallow and deep, load-bearing foundations for a variety of building and structural types. Its unique presentation focuses on new developments in computer-aided analysis and soil-structure interaction, including foundations as deformable bodies. Written by the world's leading foundation engineers, Analysis and Design of Shallow and Deep Foundations covers everything from soil investigations and loading analysis to major types of foundations and construction methods. It also features: * Coverage on computer-assisted analytical methods, balanced with standard methods such as site visits and the role of engineering geology * Methods for computing the capacity and settlement of both shallow and deep foundations * Field-testing methods and sample case studies, including projects where foundations have failed, supported with analyses of the failure * CD-ROM containing demonstration versions of analytical geotechnical software from Ensoft, Inc. tailored for use by students in the classroom

Download or read book Current Practices and Future Trends in Deep Foundations written by Jerry A. DiMaggio and published by . This book was released on 2004 with total page 514 pages. Available in PDF, EPUB and Kindle. Book excerpt: GSP 125 contains 26 papers on state-of-the-art developments in deep foundation collected in honor of George G. Goble, Ph.D., P.E.

Download or read book Contemporary Topics in Deep Foundations written by Magued Iskander and published by . This book was released on 2009 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: GSP 185 contains 80 papers presented at the International Foundation Congress and Equipment Expo held in Orlando, Florida, March 15-19, 2009.

Download or read book Foundations of Machine Learning second edition written by Mehryar Mohri and published by MIT Press. This book was released on 2018-12-25 with total page 505 pages. Available in PDF, EPUB and Kindle. Book excerpt: A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VC-dimension; Support Vector Machines (SVMs); kernel methods; boosting; on-line learning; multi-class classification; ranking; regression; algorithmic stability; dimensionality reduction; learning automata and languages; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes provide additional material including concise probability review. This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition.

Download or read book An Introduction to Deep Foundations and Sheet piling written by Donovan Henry Lee and published by . This book was released on 1961 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Design of Foundation Systems written by N. P. Kurian and published by Alpha Science Int'l Ltd.. This book was released on 2005 with total page 862 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook first published in 1992 now appearing in its third edition retains the best features from the earlier editions and adds significantly to the contents, which include developments in the 1990s.

Download or read book Introduction to Deep Learning written by Eugene Charniak and published by MIT Press. This book was released on 2019-01-29 with total page 187 pages. Available in PDF, EPUB and Kindle. Book excerpt: A project-based guide to the basics of deep learning. 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.

Download or read book Deep Learning for Coders with fastai and PyTorch written by Jeremy Howard and published by O'Reilly Media. This book was released on 2020-06-29 with total page 624 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala

Download or read book The Deep Mixing Method written by Masaki Kitazume and published by CRC Press. This book was released on 2013-02-21 with total page 434 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Deep Mixing Method (DMM), a deep in-situ soil stabilization technique using cement and/or lime as a stabilizing agent, was developed in Japan and in the Nordic countries independently in the 1970s. Numerous research efforts have been made in these areas investigating properties of treated soil, behavior of DMM improved ground under static and d

Download or read book Learning Deep Architectures for AI written by Yoshua Bengio and published by Now Publishers Inc. This book was released on 2009 with total page 145 pages. Available in PDF, EPUB and Kindle. Book excerpt: Theoretical results suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g. in vision, language, and other AI-level tasks), one may need deep architectures. Deep architectures are composed of multiple levels of non-linear operations, such as in neural nets with many hidden layers or in complicated propositional formulae re-using many sub-formulae. Searching the parameter space of deep architectures is a difficult task, but learning algorithms such as those for Deep Belief Networks have recently been proposed to tackle this problem with notable success, beating the state-of-the-art in certain areas. This paper discusses the motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer models such as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks.

Download or read book Introduction to Deep Learning written by Sandro Skansi and published by Springer. This book was released on 2018-02-04 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook presents a concise, accessible and engaging first introduction to deep learning, offering a wide range of connectionist models which represent the current state-of-the-art. The text explores the most popular algorithms and architectures in a simple and intuitive style, explaining the mathematical derivations in a step-by-step manner. The content coverage includes convolutional networks, LSTMs, Word2vec, RBMs, DBNs, neural Turing machines, memory networks and autoencoders. Numerous examples in working Python code are provided throughout the book, and the code is also supplied separately at an accompanying website. Topics and features: introduces the fundamentals of machine learning, and the mathematical and computational prerequisites for deep learning; discusses feed-forward neural networks, and explores the modifications to these which can be applied to any neural network; examines convolutional neural networks, and the recurrent connections to a feed-forward neural network; describes the notion of distributed representations, the concept of the autoencoder, and the ideas behind language processing with deep learning; presents a brief history of artificial intelligence and neural networks, and reviews interesting open research problems in deep learning and connectionism. This clearly written and lively primer on deep learning is essential reading for graduate and advanced undergraduate students of computer science, cognitive science and mathematics, as well as fields such as linguistics, logic, philosophy, and psychology.

Download or read book Foundation Engineering for Expansive Soils written by John D. Nelson and published by John Wiley & Sons. This book was released on 2015-02-10 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: Your guide to the design and construction of foundations on expansive soils Foundation Engineering for Expansive Soils fills a significant gap in the current literature by presenting coverage of the design and construction of foundations for expansive soils. Written by an expert author team with nearly 70 years of combined industry experience, this important new work is the only modern guide to the subject, describing proven methods for identifying and analyzing expansive soils and developing foundation designs appropriate for specific locations. Expansive soils are found worldwide and are the leading cause of damage to structural roads. The primary problem that arises with regard to expansive soils is that deformations are significantly greater than in non-expansive soils and the size and direction of the deformations are difficult to predict. Now, Foundation Engineering for Expansive Soils gives engineers and contractors coverage of this subject from a design perspective, rather than a theoretical one. Plus, they'll have access to case studies covering the design and construction of foundations on expansive salts from both commercial and residential projects. Provides a succinct introduction to the basics of expansive soils and their threats Includes information on both shallow and deep foundation design Profiles soil remediation techniques, backed-up with numerous case studies Covers the most commonly used laboratory tests and site investigation techniques used for establishing the physical properties of expansive soils If you're a practicing civil engineer, geotechnical engineer or contractor, geologist, structural engineer, or an upper-level undergraduate or graduate student of one of these disciplines, Foundation Engineering for Expansive Soils is a must-have addition to your library of resources.

Download or read book Statistical Foundations of Data Science written by Jianqing Fan and published by CRC Press. This book was released on 2020-09-21 with total page 974 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical Foundations of Data Science gives a thorough introduction to commonly used statistical models, contemporary statistical machine learning techniques and algorithms, along with their mathematical insights and statistical theories. It aims to serve as a graduate-level textbook and a research monograph on high-dimensional statistics, sparsity and covariance learning, machine learning, and statistical inference. It includes ample exercises that involve both theoretical studies as well as empirical applications. The book begins with an introduction to the stylized features of big data and their impacts on statistical analysis. It then introduces multiple linear regression and expands the techniques of model building via nonparametric regression and kernel tricks. It provides a comprehensive account on sparsity explorations and model selections for multiple regression, generalized linear models, quantile regression, robust regression, hazards regression, among others. High-dimensional inference is also thoroughly addressed and so is feature screening. The book also provides a comprehensive account on high-dimensional covariance estimation, learning latent factors and hidden structures, as well as their applications to statistical estimation, inference, prediction and machine learning problems. It also introduces thoroughly statistical machine learning theory and methods for classification, clustering, and prediction. These include CART, random forests, boosting, support vector machines, clustering algorithms, sparse PCA, and deep learning.

Download or read book Soil Mechanics and Foundation Engineering Fundamentals and Applications written by Nagaratnam Sivakugan and published by McGraw Hill Professional. This book was released on 2021-07-16 with total page 638 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn the basics of soil mechanics and foundation engineering This hands-on guide shows, step by step, how soil mechanics principles can be applied to solve geotechnical and foundation engineering problems. Presented in a straightforward, engaging style by an experienced PE, Soil Mechanics and Foundation Engineering: Fundamentals and Applications starts with the basics, assuming no prior knowledge, and gradually proceeds to more advanced topics. You will get rich illustrations, worked-out examples, and real-world case studies that help you absorb the critical points in a short time. Coverage includes: Phase relations Soil classification Compaction Effective stresses Permeability and seepage Vertical stresses under loaded areas Consolidation Shear strength Lateral earth pressures Site investigation Shallow and deep foundations Earth retaining structures Slope stability Reliability-based design

Download or read book Deep Foundations 2002 written by Michael W. O'Neill and published by American Society of Civil Engineers. This book was released on 2002 with total page 892 pages. Available in PDF, EPUB and Kindle. Book excerpt: Proceedings of the International Deep Foundations Congress 2002, held in Orlando, Florida, February 14-16, 2002. Sponsored by The Geo-Institute of ASCE. This Geotechnical Special Publication contains 110 papers documenting applied research and engineering experience in the area of deep foundations. The volume is a comprehensive resource for both researchers and practitioners covering driven, jacked, and augered piles and drilled shafts. Topics include: geotechnical design, structural design, innovative construction, validation and verification of design and construction, soil-structure interaction, reliability-based design, field load testing for design, concepts for deep foundation systems (such as piled rafts), numerical and analytical modeling of pile foundations, design of foundations for extreme events, and numerous and varied case histories. Several papers also focus on the acquisition and use of geomaterial properties for deep foundation design and the use of deep foundations in walls.