the Nest of Students

Dennis G. Zill ... 1024 pages - Publisher: Jones & Bartlett Learning; 6th edition (September, 2016) ... Language: English - ISBN-10: 1284105903 - ISBN-13: 978-1284105902 ...

Modern and comprehensive, the new sixth edition of award-winning author, Dennis G. Zill's Advanced Engineering Mathematics is a compendium of topics that are most often covered in courses in engineering mathematics, and is extremely flexible to meet the unique needs of courses ranging from ordinary differential equations, to vector calculus, to partial differential equations. A key strength of this best-selling text is the author's emphasis on differential equations as mathematical models, discussing the constructs and pitfalls of each. An accessible writing style and robust pedagogical aids guide students through difficult concepts with thoughtful explanations, clear examples, interesting applications, and contributed project problems.

Darko Dujmovic,‎ Boris Androic,‎ Ivan Lukacevic ... 924 pages - Publisher: Ernst&Sohn; 1st edition (April, 2015) ... Language: English - ISBN-10: 343303107X - ISBN-13: 978-3433031070 ...

The use of composite structures in construction is increasing. The optimized combination of the two materials concrete and steel produces particularly cost-efficient structures. This book presents a large number of numerical examples with detailed explanations of the provisions of Eurocode 4. It deals with the most common structural components in building construction: beams, columns and slabs. Furthermore, comprehensive chapters provide insight into the topics of creep and shrinkage, as well as fatigue. This book enables the reader to efficiently perform analyses of composite structures. It is a valuable reference book for professionals as well as an outstanding means for students to become familiar with the Eurocode 4.

A recurrent neural network (RNN) is a type of advanced artificial neural network (ANN) that involves directed cycles in memory. One aspect of recurrent neural networks is the ability to build on earlier types of networks with fixed-size input vectors and output vectors. The use of recurrent neural networks are often related to deep learning and the use of sequences to evolve models that simulate the neural activity in the human brain. In terms of practical application, RNNs have been an active area of focus for many professionals for uses like image processing, language processing, and even models that add characters to text one at a time. By playing around with these text generation models, scientists have been able to produce samples that look a lot like different kinds of human writing – for example, modern investment op-eds, or classical Shakespeare plays. The RNN has been able to generate text results that demonstrate the ability to learn English from scratch, or from very limited programming inputs. Many examples of using RNNs produce text that is not grammatically correct. The idea is that a large number of these experiments and systems need additional supports to really become useful – but they do demonstrate amazing artificial intelligence power to model the human generation of language.

George E. P. Box,‎ George C. Tiao ... 588 pages - Publisher: Addison-Wesley Publishing; 1st edition (June, 1973) ... Language: English - ISBN-10: 0201006227 - ISBN-13: 978-0201006223 ... Its main objective is to examine the application and relevance of Bayes' theorem to problems that arise in scientific investigation in which inferences must be made regarding parameter values about which little is known a prior. Begins with a discussion of some important general aspects of the Bayesian approach such as the choice of prior distribution, particularly non-informative prior distribution, the problem of nuisance parameters and the role of sufficient statistics, followed by many standard problems concerned with the comparison of location and scale parameters. The main thrust is an investigation of questions with appropriate analysis of mathematical results which are illustrated with numerical examples, providing evidence of the value of the Bayesian approach.

George C. Lee, Ernest Sternberg ... 180 pages - Publisher: State Univ. of New York Press (March, 2015) ... Language: English - ISBN-10: 1438455267 - ISBN-13: 978-1438455266 ...

Whether you are a student considering a career in civil engineering and transportation planning, a public official interested in the future of infrastructure, or a person who simply cares about bridges, this book offers an accessible and illustrated introduction to the most beloved feature of our built environment. Learn about engineering basics: the forces that bridges must resist to stay aloft and the principles by which engineers decide which types of bridges make sense at which sites. Find out how engineers protect bridges from their greatest threats—the earthquakes, floods, and other hazards that can cause catastrophic damage. Moving from engineering to planning, learn how we decide whether a bridge is worth building in the first place, learn about controversial features of cost-benefit analysis, and about the transportation models by which planners forecast bridge effects on traffic patterns. Investigate a sometimes intractable problem: why a project often creeps along for a decade or more to get from initial studies to the day the ribbon is cut, undergoing vast cost escalations. Also explore the environmental impact of bridges, and the meaning of a “sustainable bridge,” and whether bridges could once again be built, like ancient Roman ones, to last a thousand years.

Convolutional networks (CNNs) are deep artificial neural networks that can be used to classify images (name what they see), cluster them by similarity (photo search), and perform object recognition within scenes. They are algorithms that can identify faces, individuals, street signs, eggplants, platypuses and many other aspects of visual data. Convolutional networks perform optical character recognition (OCR) to digitize text and make natural-language processing possible on analog and hand-written documents, where the images are symbols to be transcribed. CNNs can also be applied to sound when it is represented visually as a spectrogram. More recently, convolutional networks have been applied directly to text analytics as well as graph data with graph convolutional networks. The efficacy of convolutional nets (ConvNets or CNNs) in image recognition is one of the main reasons why the world has woken up to the efficacy of deep learning. They are powering major advances in machine vision, which has obvious applications for self-driving cars, robotics, drones, security, medical diagnoses, and treatments for the visually impaired.