Geo.Engineerings

Ferdinand P. Beer, E. Russell Johnston, Jr., John T. Dewolf, David F. Mazurek ... 832 pages - Publisher: McGraw-Hill; 6th edition (January, 2011) ... Language: English.

Beer and Johnston's Mechanics of Materials is the uncontested leader for the teaching of solid mechanics. Used by thousands of students around the globe since its publication in 1981, Mechanics of Materials, provides a precise presentation of the subject illustrated with numerous engineering examples that students both understand and relate to theory and application. The tried and true methodology for presenting material gives your student the best opportunity to succeed in this course. From the detailed examples, to the homework problems, to the carefully developed solutions manual, you and your students can be confident the material is clearly explained and accurately represented. If you want the best book for your students, we feel Beer, Johnston's Mechanics of Materials, 6th edition is your only choice.

Makoto Ohsaki, Kiyohiro Ikeda ... 269 pages - Publisher: Springer; (June, 2007) ... Language: English - ISBN-10: 0387681833 - ISBN-13: 978-0567088147 ...

This book focuses on the optimization of a geometrically-nonlinear structure under stability constraint. It presents a deep insight into optimization-based and computer-assisted stability design of discrete structures. Coverage combines design sensitivity analysis developed in structural optimization and imperfection-sensitivity analysis developed in stability analysis.

Klaus-Jurgen Bathe ... 1037 pages - Publisher: Prentice Hall; (1996) ... Language: English - ISBN-10: 8126529989 - ISBN-13: 978-8126529988

For courses in finite element methods, finite element analysis taught in departments of Civil, Mechanical, Aerospace, Agriculture, and Mechanics departments. Course for which this book is appropriate is usually taught to seniors or graduate students. Comprehensive: this text explores the full range of finite element methods used in engineering practice for actual applications in computer-aided design. It provides not only an introduction to finite element methods and the commonality in the various techniques, but explores state-of-the-art methods as well with a focus on what are deemed to become "classical techniques" procedures that will be standard and authoritative for finite element analysis for years to come.

Xian Liu, Wei Wu, Fulvio Tonon ... 382 pages - Publisher: ASCE; (May, 2010) ... Language: English - ISBN-10: 0784411077 - ISBN-13: 978-0784411070 ...

The pressure exerted by the population increase, the sensitivity toward the environment, and the ever-increasing cost of the land, are just some of the reasons why underground excavations are necessary to societys health and future providing room for services, transportation of people and goods, water supply and disposal, sanitation, and storage.

The topics in this course come from an analysis of real requirements in data scientist job listings from the biggest tech employers. We'll cover the machine learning, AI, and data mining techniques real employers are looking for, including: Deep Learning / Neural Networks (MLP's, CNN's, RNN's) with TensorFlow and Keras + Sentiment analysis + Image recognition and classification + Regression analysis + K-Means Clustering + Principal Component Analysis + Train/Test and cross validation + Bayesian Methods + Decision Trees and Random Forests + Multivariate Regression + Multi-Level Models + Support Vector Machines + Reinforcement Learning + Collaborative Filtering + K-Nearest Neighbor + Bias/Variance Tradeoff + Ensemble Learning + Term Frequency / Inverse Document Frequency + Experimental Design and A/B Tests.

Machine Learning, Classification and Algorithms using MATLAB: Learn to Implement Classification Algorithms In One of the Most Power Tool used by Scientists and Engineer.

 This course is designed to cover one of the most interesting areas of machine learning called classification. I will take you step-by-step in this course and will first cover the basics of MATLAB. Following that we will look into the details of how to use different machine learning algorithms using MATLAB. Specifically, we will be looking at the MATLAB toolbox called statistic and machine learning toolbox. We will implement some of the most commonly used classification algorithms such as K-Nearest Neighbor, Naive Bayes, Discriminant Analysis, Decision Tress, Support Vector Machines, Error Correcting Output Codes and Ensembles. Following that we will be looking at how to cross validate these models and how to evaluate their performances. Intuition into the classification algorithms is also included so that a person with no mathematical background can still comprehend the essential ideas. The following are the course outlines. Segment 1: Grabbing and Importing Dataset + Segment 2: K-Nearest Neighbor + Segment 3: Naive Bayes + Segment 4: Decision Trees + Segment 5: Discriminant Analysis + Segment 6: Support Vector Machines + Segment 7: Error Correcting Output Codes + Segment 8: Classification with Ensembles + Segment 9: Validation Methods + Segment 10: Evaluating Performance.