Geo.Engineerings

Paul Kurowski ... 600 pages - Publisher: SDC Publications; (March, 2018) ... Language: English - ISBN-10: 1630571539 - ISBN-13: 978-1630571535.

Engineering Analysis with SOLIDWORKS Simulation 2018 goes beyond the standard software manual. Its unique approach concurrently introduces you to the SOLIDWORKS Simulation 2018 software and the fundamentals of Finite Element Analysis (FEA) through hands-on exercises. A number of projects are presented using commonly used parts to illustrate the analysis features of SOLIDWORKS Simulation. Each chapter is designed to build on the skills, experiences and understanding gained from the previous chapters.

Mark Fenner ... 592 pages - Publisher: Addison-Wesley Professional; (August, 2019) ... Language: English - ISBN-10: 0134845625 - ISBN-13: 978-0134845623.

The Complete Beginner’s Guide to Understanding and Building Machine Learning Systems with Python: Machine Learning with Python for Everyone will help you master the processes, patterns, and strategies you need to build effective learning systems, even if you’re an absolute beginner. If you can write some Python code, this book is for you, no matter how little college-level math you know. Principal instructor Mark E. Fenner relies on plain-English stories, pictures, and Python examples to communicate the ideas of machine learning. Mark begins by discussing machine learning and what it can do; introducing key mathematical and computational topics in an approachable manner; and walking you through the first steps in building, training, and evaluating learning systems. Step by step, you’ll fill out the components of a practical learning system, broaden your toolbox, and explore some of the field’s most sophisticated and exciting techniques. Whether you’re a student, analyst, scientist, or hobbyist, this guide’s insights will be applicable to every learning system you ever build or use.

Understand machine learning algorithms, models, and core machine learning concepts + Classify examples with classifiers, and quantify examples with regressors + Realistically assess performance of machine learning systems + Use feature engineering to smooth rough data into useful forms + Chain multiple components into one system and tune its performance + Apply machine learning techniques to images and text + Connect the core concepts to neural networks and graphical models + Leverage the Python scikit-learn library and other powerful tools.

Bofang Zhu ... 822 pages - Publisher: Wiley; (March, 2018) ... Language: English - AmazonSIN: B07C8HGH2Z.

A comprehensive review of the Finite Element Method (FEM), this book provides the fundamentals together with a wide range of applications in civil, mechanical and aeronautical engineering. It addresses both the theoretical and numerical implementation aspects of the FEM, providing examples in several important topics such as solid mechanics, fluid mechanics and heat transfer, appealing to a wide range of engineering disciplines. Written by a renowned author and academician with the Chinese Academy of Engineering, The Finite Element Method would appeal to researchers looking to understand how the fundamentals of the FEM can be applied in other disciplines. Researchers and graduate students studying hydraulic, mechanical and civil engineering will find it a practical reference text.

Stephen Emmitt ... 533 pages - Publisher: Wiley-Blackwell; 4th edition (August, 2018) ... Language: English - AmazonSIN: B07H1MCFMV.

The revised fourth edition of Barry's Advanced Construction of Buildings expands on the resource that has become a standard text on the construction of buildings. The fourth edition covers the construction of larger-scale buildings (primarily residential, commercial and industrial) constructed with load bearing frames in timber, concrete and steel; supported by chapters on offsite construction, piling, envelopes to framed buildings, fit-out and second fix, lifts and escalators, building pathology, upgrading and demolition.

The author covers the functional and performance requirements of the main building elements as well as building efficiency and information on meeting the challenges of limiting the environmental impact of buildings. Each chapter includes new "at a glance" summaries that introduce the basic material giving a good understanding of the main points quickly and easily. The text is fully up to date with the latest building regulations and construction technology. This important resource: Covers design, technology, offsite construction, site assembly and environmental issues of larger-scale buildings including primarily residential, commercial and industrial buildings constructed with load bearing frames + Highlights the concept of building efficiency, with better integration of the topics throughout the text + Offers new "at a glance" summaries at the beginning of each chapter + Is a companion to Barry's Introduction to Construction of Buildings, fourth edition. Written for undergraduate students and those working towards similar NQF level 5 and 6 qualifications in building and construction, Barry's Advanced Construction of Buildings is a practical and highly illustrated guide to construction practice. It covers the materials and technologies involved in constructing larger scale buildings.

Umberto Michelucci ... 431 pages - Publisher: Apress; (September, 2018) ... Language: English - AmazonSIN: B07H6D9NQ8.
Work with advanced topics in deep learning, such as optimization algorithms, hyper-parameter tuning, dropout, and error analysis as well as strategies to address typical problems encountered when training deep neural networks. You’ll begin by studying the activation functions mostly with a single neuron (ReLu, sigmoid, and Swish), seeing how to perform linear and logistic regression using TensorFlow, and choosing the right cost function. The next section talks about more complicated neural network architectures with several layers and neurons and explores the problem of random initialization of weights. An entire chapter is dedicated to a complete overview of neural network error analysis, giving examples of solving problems originating from variance, bias, overfitting, and datasets coming from different distributions. Applied Deep Learning also discusses how to implement logistic regression completely from scratch without using any Python library except NumPy, to let you appreciate how libraries such as TensorFlow allow quick and efficient experiments. Case studies for each method are included to put into practice all theoretical information. You’ll discover tips and tricks for writing optimized Python code (for example vectorizing loops with NumPy).

What You Will Learn: Implement advanced techniques in the right way in Python and TensorFlow + Debug and optimize advanced methods (such as dropout and regularization) + Carry out error analysis (to realize if one has a bias problem, a variance problem, a data offset problem, and so on) + Set up a machine learning project focused on deep learning on a complex dataset. Who This Book Is For: Readers with a medium understanding of machine learning, linear algebra, calculus, and basic Python programming.

Lisa Oberbroeckling ... 296 pages - Publisher: Academic Press; (June, 2020) ... Language: English - ISBN-10: 0128177993 - ISBN-13: 978-0128177990.

Providing an alternative to engineering-focused resources in the area, Programming Mathematics Using MATLAB® introduces the basics of programming and of using MATLAB® by highlighting many mathematical examples. Emphasizing mathematical concepts through the visualization of programming throughout the book, this useful resource utilizes examples that may be familiar to math students (such as numerical integration) and others that may be new (such as fractals). Additionally, the text uniquely offers a variety of MATLAB® projects, all of which have been class-tested thoroughly, and which enable students to put MATLAB® programming into practice while expanding their comprehension of concepts such as Taylor polynomials and the Gram–Schmidt process. Programming Mathematics Using MATLAB® is appropriate for readers familiar with sophomore-level mathematics (vectors, matrices, multivariable calculus), and is useful for math courses focused on MATLAB® specifically and those focused on mathematical concepts which seek to utilize MATLAB® in the classroom.