Geo.Engineerings

Through ten editions, Fox and McDonald's Introduction to Fluid Mechanics has helped students understand the physical concepts, basic principles, and analysis methods of fluid mechanics. This market-leading textbook provides a balanced, systematic approach to mastering critical concepts with the proven Fox-McDonald solution methodology. In-depth yet accessible chapters present governing equations, clearly state assumptions, and relate mathematical results to corresponding physical behavior. Emphasis is placed on the use of control volumes to support a practical, theoretically-inclusive problem-solving approach to the subject. Each comprehensive chapter includes numerous, easy-to-follow examples that illustrate good solution technique and explain challenging points. A broad range of carefully selected topics describe how to apply the governing equations to various problems, and explain physical concepts to enable students to model real-world fluid flow situations. Topics include flow measurement, dimensional analysis and similitude, flow in pipes, ducts, and open channels, fluid machinery, and more. To enhance student learning, the book incorporates numerous pedagogical features including chapter summaries and learning objectives, end-of-chapter problems, useful equations, and design and open-ended problems that encourage students to apply fluid mechanics principles to the design of devices and systems.

Indrajit Chowdhury, Shambhu P. Dasgupta ... 882 pages - Publisher: CRC Press; (December, 2008) ... Language: English - ISBN-10: 0415471451 - ISBN-13: 978-0415471459.

Designed to provide engineers with quick access to current and practical information on the dynamics of structure and foundation, this unique work, consisting of two separately available volumes, serves as a complete reference, especially for those involved with earthquake or dynamic analysis, or the design of machine foundations in the oil, gas, and energy sector. This first volume deals with theories and formulations, covering the full range of topics involved and dynamics of structure and foundation. It specifically focuses on a unified approach in dealing with dynamic soul-structure interaction and geotechnical considerations for dynamic soil-structure interaction. The authors present new insights and theories, such as the computation of Rayleigh damping for structures with a large number of degrees of freedom, and the dynamic analysis of Hammer foundations, considering non-classical soil damping.

In a clear style, this well-illustrated column addresses detailed topics, grouped in the following major themes: Elasticity and numerical methods in engineering + Lumped parameter vibration + Soil-structure systems under static load + Structural and soil dynamics. This reference and design guide is intended for academics and professionals in civil and structural engineering involved with earthquake or dynamic analysis or the design of machine foundations. In combination with the Applications book (volume 2), it could be used as course material for advanced university and professional education in structural dynamics, soil dynamics, analysis and design of machined foundations, and earthquake engineering.

Machine learning has gained tremendous popularity for its powerful and fast predictions with large datasets. However, the true forces behind its powerful output are the complex algorithms involving substantial statistical analysis that churn large datasets and generate substantial insight. This second edition of Machine Learning Algorithms walks you through prominent development outcomes that have taken place relating to machine learning algorithms, which constitute major contributions to the machine learning process and help you to strengthen and master statistical interpretation across the areas of supervised, semi-supervised, and reinforcement learning. Once the core concepts of an algorithm have been covered, you’ll explore real-world examples based on the most diffused libraries, such as scikit-learn, NLTK, TensorFlow, and Keras. You will discover new topics such as principal component analysis (PCA), independent component analysis (ICA), Bayesian regression, discriminant analysis, advanced clustering, and gaussian mixture.

By the end of this book, you will have studied machine learning algorithms and be able to put them into production to make your machine learning applications more innovative. What you will learn: Study feature selection and the feature engineering process + Assess performance and error trade-offs for linear regression + Build a data model and understand how it works by using different types of algorithm + Learn to tune the parameters of Support Vector Machines (SVM) + Explore the concept of natural language processing (NLP) and recommendation systems + Create a machine learning architecture from scratch. Who this book is for: Machine Learning Algorithms is for you if you are a machine learning engineer, data engineer, or junior data scientist who wants to advance in the field of predictive analytics and machine learning. Familiarity with R and Python will be an added advantage for getting the best from this book.