The Nest of Engineers

Future microwave, wireless communication systems, computer chip designs, and sensor systems will require miniature fabrication processes in the order of nanometers or less as well as the fusion of various material technologies to produce composites consisting of many different materials. This requires distinctly multidisciplinary collaborations, implying that specialized approaches will not be able to address future world markets in communication, computer, and electronic miniaturized products.

Anticipating that many students lack specialized simultaneous training in magnetism and magnetics, as well as in other material technologies, Magnetics, Dielectrics, and Wave Propagation with MATLABR Codes avoids application-specific descriptions, opting for a general point of view of materials per se. Specifically, this book develops a general theory to show how a magnetic system of spins is coupled to acoustic motions, magnetoelectric systems, and superconductors. Phenomenological approaches are connected to atomic-scale formulations that reduce complex calculations to essential forms and address basic interactions at any scale of dimensionalities. With simple and clear coverage of everything from first principles to calculation tools, the book revisits fundamentals that govern magnetic, acoustic, superconducting, and magnetoelectric motions at the atomic and macroscopic scales, including superlattices. Constitutive equations in Maxwell’s equations are introduced via general free energy expressions which include magnetic parameters as well as acoustic, magnetoelectric, semiconductor, and superconducting parameters derived from first principles. More importantly, this book facilitates the derivation of these parameters, as the dimensionality of materials is reduced toward the microscopic scale, thus introducing new concepts. The deposition of ferrite films at the atomic scale complements the approach toward the understanding of the physics of miniaturized composites. Thus, a systematic formalism of deriving the permeability or the magnetoelectric coupling tensors from first principles, rather than from an ad hoc approach, bridges the gap between microscopic and macroscopic principles as applied to wave propagation and other applications.

Bayesian methods of inference are deeply natural and extremely powerful. However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice–freeing you to get results using computing power. Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention.

Davidson-Pilon begins by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, he introduces PyMC through a series of detailed examples and intuitive explanations that have been refined after extensive user feedback. You’ll learn how to use the Markov Chain Monte Carlo algorithm, choose appropriate sample sizes and priors, work with loss functions, and apply Bayesian inference in domains ranging from finance to marketing. Once you’ve mastered these techniques, you’ll constantly turn to this guide for the working PyMC code you need to jumpstart future projects.

Coverage includes: • Learning the Bayesian “state of mind” and its practical implications • Understanding how computers perform Bayesian inference • Using the PyMC Python library to program Bayesian analyses • Building and debugging models with PyMC • Testing your model’s “goodness of fit” • Opening the “black box” of the Markov Chain Monte Carlo algorithm to see how and why it works • Leveraging the power of the “Law of Large Numbers” • Mastering key concepts, such as clustering, convergence, autocorrelation, and thinning • Using loss functions to measure an estimate’s weaknesses based on your goals and desired outcomes • Selecting appropriate priors and understanding how their influence changes with dataset size • Overcoming the “exploration versus exploitation” dilemma: deciding when “pretty good” is good enough • Using Bayesian inference to improve A/B testing • Solving data science problems when only small amounts of data are available.

Coverage includes: Algorithms + Objects + Concurrency + Collaboration + Built-in modules + Production techniques + And more. Each section contains specific, actionable guidelines organized into items, each with carefully worded advice supported by detailed technical arguments and illuminating examples. Using Effective Python, you can systematically improve all the Python code you write: not by blindly following rules or mimicking incomprehensible idioms, but by gaining a deep understanding of the technical reasons why they make sense.

Galit Shmueli, Kenneth C. Lichtendahl Jr. ... 232 pages - Publisher: Axelrod Schnall Publishers; 2nd edition (July, 2016) ... Language: English - ISBN-10: 0997847913 - ISBN-13: 978-0997847918. 

Practical Time Series Forecasting with R: A Hands-On Guide, 2nd Edition provides an applied approach to time-series forecasting. Forecasting is an essential component of predictive analytics. The book introduces popular forecasting methods and approaches used in a variety of business applications. The book offers clear explanations, practical examples, and end-of-chapter exercises and cases. Readers will learn to use forecasting methods using the free open-source R software to develop effective forecasting solutions that extract business value from time-series data.

Featuring improved organization and new material, the Second Edition also includes: Popular forecasting methods including smoothing algorithms, regression models, and neural networks + A practical approach to evaluating the performance of forecasting solutions + A business-analytics exposition focused on linking time-series forecasting to business goals + Guided cases for integrating the acquired knowledge using real data + End-of-chapter problems to facilitate active learning + A companion site with data sets, R code, learning resources, and instructor materials (solutions to exercises, case studies)Globally-available textbook, available in both softcover and Kindle formats

Practical Time Series Forecasting with R: A Hands-On Guide, Second Edition is the perfect textbook for upper-undergraduate, graduate and MBA-level courses as well as professional programs in data science and business analytics. The book is also designed for practitioners in the fields of operations research, supply chain management, marketing, economics, finance and management.

This book provides comprehensive details of all Swarm Intelligence based Techniques available till date in a comprehensive manner along with their mathematical proofs. It will act as a foundation for authors, researchers and industry professionals. This monograph will present the latest state of the art research being done on varied Intelligent Technologies like sensor networks, machine learning, optical fiber communications, digital signal processing, image processing and many more.

Walter Savitch, Kenrick Mock ... 984 pages - Publisher: Pearson; 5th edition (March, 2012) ... Language: English - ISBN-10: 013283071X - ISBN-13: 978-0132830713

Praised for providing an engaging balance of thoughtful examples and explanatory discussion, best-selling author Walter Savitch explains concepts and techniques in a straightforward style using understandable language and code enhanced by a suite of pedagogical tools. Absolute C++ is appropriate for both introductory and intermediate C++ programmers. This edition is available with MyProgrammingLab, an innovative online homework and assessment tool. Through the power of practice and immediate personalized feedback, MyProgrammingLab helps students fully grasp the logic, semantics, and syntax of programming.

Ke-Lin Du, M. N. S. Swamy ... 489 pages - Publisher: Springer; (December, 2016) ... Language: English - ISBN-10: 3319454013 - ISBN-13: 978-3319454016.

This textbook provides a comprehensive introduction to nature-inspired metaheuristic methods for search and optimization, including the latest trends in evolutionary algorithms and other forms of natural computing.  Over 100 different types of these methods are discussed in detail.  The authors emphasize non-standard optimization problems and utilize a natural approach to the topic, moving from basic notions to more complex ones. An introductory chapter covers the necessary biological and mathematical backgrounds for understanding the main material.  Subsequent chapters then explore almost all of the major metaheuristics for search and optimization created based on natural phenomena, including simulated annealing, recurrent neural networks, genetic algorithms and genetic programming, differential evolution, memetic algorithms, particle swarm optimization, artificial immune systems, ant colony optimization, tabu search and scatter search, bee and bacteria foraging algorithms, harmony search, biomolecular computing, quantum computing, and many others.  General topics on dynamic, multimodal, constrained, and multiobjective optimizations are also described.  Each chapter includes detailed flowcharts that illustrate specific algorithms and exercises that reinforce important topics.  Introduced in the appendix are some benchmarks for the evaluation of metaheuristics. Search and Optimization by Metaheuristics is intended primarily as a textbook for graduate and advanced undergraduate students specializing in engineering and computer science.  It will also serve as a valuable resource for scientists and researchers working in these areas, as well as those who are interested in search and optimization methods.