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Internal Combustion Engine Handbook
(SAE international, 2004) Richard van Basshuysen; Fred Schafer
Thorough in its presentation, this essential resource illustrates the latest level of knowledge in engine development, paying particular attention to the presentation of theory and practice in a balanced ratio. Almost 950 pages in length - with 1,250 illustrations and nearly 700 bibliographical references - the Internal Combustion Engine Handbook covers all of this component's complexities, including an insightful look into the internal combustion engine's future viability. An ideal publication for specialists in the automotive, engine, mineral oil, and accessories industries, this book will also prove to be useful for students, patent lawyers, the motor vehicle trade, government offices, journalists, and interested members of the public.
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University Physics with Modern Physics
(Pearson, 2012) Young Hugh. D; Freedman Roger A.; Lewis Ford A.
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Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow:
(O’Reilly Media, Inc.,, 2019) Aurélien Géron
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworksâ??Scikit-Learn and TensorFlowâ??author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. Youâ learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what youâ??ve learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use Scikit-Learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets
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Computational fluid dynamics
(McGraw-Hill, 1995) Anderson, John David
Basic Philosophy of CFD.- 2 Governing Equations of Fluid Dynamics.- 3 Incompressible Inviscid source and vortex panel methods.- 4 Mathematical Properties of the Fluid Dynamic Equations.- 5 Discretization of Partial Differential Equations.- 6 Transformations and Grids.- 7 Explicit Finite Difference Some Selected Applications to Inviscid and Viscous Flows.- 2.- 8 Boundary Layer Equations and Methods of Solution.- 9 Implicit Time-Dependent Methods for Inviscid and Viscous Compressible Flows, With a Discussion of the Concept of Numerical Dissipation.- 10 Introduction to Finite Element Techniques in Computational Fluid Dynamics.- 11 Introduction to Finite Volume Techniques in Computational Fluid Dynamics.
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Mine Managers' handbook
(The Australasian Institute of Mining and Metallurgy, 2012)
The Mine Managers' Handbook is a comprehensive volume describing all the important aspects of managing a mining company. It addresses topics such as corporate strategy, leadership, operations management, and world markets for minerals. Written by experts from industry, consulting firms, and academia, it will be of great value to corporate executives, directors of mining companies, students, and to all those wishing to understand the industry. While written primarily for Australian conditions, much of the content applies to the global minerals industry. Chapters include: Overview of mine management, Occupational health and safety, Environment management, Stakeholder relationships, Human resources, Capital investment and project development, Operations management, Finance and administration, Minerals and markets, Strategic planning. Also included is a CD that covers Guidelines for Technical Economic Evaluations of Minerals Industry Projects.