The Nest of Engineers

Python is one of the most widely used programming languages in the world. It is used everywhere from primary school education to workaday web development, to the most advanced scientific research institutes of the world. However, like all programming languages, Python has a collection of "Pythonic" ways of accomplishing tasks that are easy to overlook, especially when habits are borrowed wholesale from work in other programming languages. Better Python Code is a guide to Pythonic programming. The book presents common mistakes that Python developers make--even Python developers who have used the language for years--often because Python sometimes presents "attractive nuisances." Throughout, the book is a guide to better programming in the core Python language.

Each section shows a concrete but concise example of some misunderstanding or bad habit in action. Each section contains a description of what is wrong with the sample code and a suggestion for one or more better ways to code equivalent functionality without the initial pitfall. Every pitfall addressed in this book reflects foibles, errors, and misunderstandings that the author as seen in concrete, widely used code bases written by experienced developers, over his 25 years of writing Python. The sections of this book each present some mistake, pitfall, or foible that developers can easily fall into, and are accompanied by descriptions of ways to avoid making them. At times those solutions simply involve a minor change in “spelling,” but in most cases they require a nuance of thought and design in your code. Many of the discussions do something else as well…

Lars Grüne,‎ Jürgen Pannek ... 456 pages - Publisher: Springer; 2nd edition (November 11, 2016) - Language: English - ISBN-10: 3319460234 - ISBN-13: 978-3319460239.

This book offers readers a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-data systems. NMPC schemes with and without stabilizing terminal constraints are detailed, and intuitive examples illustrate the performance of different NMPC variants. NMPC is interpreted as an approximation of infinite-horizon optimal control so that important properties like closed-loop stability, inverse optimality and suboptimality can be derived in a uniform manner. These results are complemented by discussions of feasibility and robustness. An introduction to nonlinear optimal control algorithms yields essential insights into how the nonlinear optimization routine―the core of any nonlinear model predictive controller―works. Accompanying software in MATLAB® and C++ (downloadable from extras.springer.com/), together with an explanatory appendix in the book itself, enables readers to perform computer experiments exploring the possibilities and limitations of NMPC. The second edition has been substantially rewritten, edited and updated to reflect the significant advances that have been made since the publication of its predecessor, including: • a new chapter on economic NMPC relaxing the assumption that the running cost penalizes the distance to a pre-defined equilibrium; • a new chapter on distributed NMPC discussing methods which facilitate the control of large-scale systems by splitting up the optimization into smaller subproblems; • an extended discussion of stability and performance using approximate updates rather than full optimization; • replacement of the pivotal sufficient condition for stability without stabilizing terminal conditions with a weaker alternative and inclusion of an alternative and much simpler proof in the analysis; and • further variations and extensions in response to suggestions from readers of the first edition. Though primarily aimed at academic researchers and practitioners working in control and optimization, the text is self-contained, featuring background material on infinite-horizon optimal control and Lyapunov stability theory that also makes it accessible for graduate students in control engineering and applied mathematics.

Noah Gift ... 272 pages - Publisher: Addison-Wesley Professional; (September, 2018) ... Language: English - ISBN-10: 0134863860 - ISBN-13: 978-0134863863.

Master Powerful Off-the-Shelf Business Solutions for AI and Machine Learning: Pragmatic AI will help you solve real-world problems with contemporary machine learning, artificial intelligence, and cloud computing tools. Noah Gift demystifies all the concepts and tools you need to get results—even if you don’t have a strong background in math or data science. Gift illuminates powerful off-the-shelf cloud offerings from Amazon, Google, and Microsoft, and demonstrates proven techniques using the Python data science ecosystem. His workflows and examples help you streamline and simplify every step, from deployment to production, and build exceptionally scalable solutions. As you learn how machine language (ML) solutions work, you’ll gain a more intuitive understanding of what you can achieve with them and how to maximize their value.

Jordan Radosavljević ... 536 pages - Publisher: The Institution of Engineering and Technology (June, 2018) ... Language: English - ISBN-10: 1785615467 - ISBN-13: 978-1785615467.

A metaheuristic is a consistent set of ideas, concepts, and operators to design a heuristic optimization algorithm, that can provide a sufficiently good solution to an optimization problem with incomplete or imperfect information. Modern and emerging power systems, with the growing complexity of distributed and intermittent generation, are an important application for such methods. This book describes the principles of solving various problems in power engineering via the application of selected metaheuristic optimization methods including genetic algorithms, particle swarm optimization, and the gravitational search algorithm. Applications covered include power flow calculation; optimal power flow in transmission networks; optimal reactive power dispatch in transmission networks; combined economic and emission dispatch; optimal power flow in distribution networks; optimal volt/var control in distribution networks; optimal placement and sizing of distributed generation in distribution networks; optimal energy and operation management of microgrids; optimal coordination of directional overcurrent relays; and steady-state analysis of self-excited induction generators.

Dan Stefanoiu, Pierre Borne, Dumitru Popescu, Florin Gheorghe Filip, Abdelkader El Kamel ... 445 pages - Publisher: Wiley-ISTE; (October, 2014) ... Language: English - AmazonSIN: B00P3W32R8.

The purpose of this book is to present the main metaheuristics and approximate and stochastic methods for optimization of complex systems in Engineering Sciences. It has been written within the framework of the European Union project ERRIC, which is funded by the EU’s FP7 Research Potential program and has been developed in co-operation between French and Romanian teaching researchers. Through the principles of various proposed algorithms (with additional references) this book allows the reader to explore various methods of implementation such as metaheuristics, local search and populationbased methods. It examines multi-objective and stochastic optimization, as well as methods and tools for computer-aided decision-making and simulation for decision-making.

Martin H. Trauth ... 529 pages - Publisher: Springer; 5th edition (December, 2020) - Language: English ... ISBN-10: 3030384403 - ISBN-13: 978-3030384401.

MATLAB is used in a wide range of geoscientific applications, e.g. for image processing in remote sensing, for creating and processing digital elevation models, and for analyzing time series. This book introduces readers to MATLAB-based data analysis methods used in the geosciences, including basic statistics for univariate, bivariate and multivariate datasets, time-series analysis, signal processing, the analysis of spatial and directional data, and image analysis. The revised and updated Fifth Edition includes seven new sections, and the majority of the chapters have been rewritten and significantly expanded. New sections include error analysis, the problem of classical linear regression of log-transformed data, aligning stratigraphic sequences, the Normalized Difference Vegetation Index, Aitchison’s log-ratio transformation, graphical representation of spherical data, and statistics of spherical data. The book also includes numerous examples demonstrating how MATLAB can be used on datasets from the earth sciences. The supplementary electronic material (available online through SpringerLink) contains recipes that include all the MATLAB commands featured in the book and the sample data.

A. Kaveh ... 426 pages - Publisher: Springer; 2nd edition (November, 2016) ... Language: English - AmazonSIN: B01MSJNVOO.

This book presents efficient metaheuristic algorithms for optimal design of structures. Many of these algorithms are developed by the author and his colleagues, consisting of Democratic Particle Swarm Optimization, Charged System Search, Magnetic Charged System Search, Field of Forces Optimization, Dolphin Echolocation Optimization, Colliding Bodies Optimization, Ray Optimization. These are presented together with algorithms which were developed by other authors and have been successfully applied to various optimization problems. These consist of Particle Swarm Optimization, Big Bang-Big Crunch Algorithm, Cuckoo Search Optimization, Imperialist Competitive Algorithm, and Chaos Embedded Metaheuristic Algorithms. Finally a multi-objective optimization method is presented to solve large-scale structural problems based on the Charged System Search algorithm. The concepts and algorithms presented in this book are not only applicable to optimization of skeletal structures and finite element models, but can equally be utilized for optimal design of other systems such as hydraulic and electrical networks. In the second edition seven new chapters are added consisting of the new developments in the field of optimization. These chapters consist of the Enhanced Colliding Bodies Optimization, Global Sensitivity Analysis, Tug of War Optimization, Water Evaporation Optimization, Vibrating Particle System Optimization and Cyclical Parthenogenesis Optimization algorithms. A chapter is also devoted to optimal design of large scale structures.

João Paulo Coelho, Tatiana M. Pinho, José Boaventura-Cunha ... 296 pages - ISBN-13: 978-0367203498 ... Publisher: CRC Press; (August, 2019) - Language: English.

This book presents, in an integrated form, both the analysis and synthesis of three different types of hidden Markov models. Unlike other books on the subject, it is generic and does not focus on a specific theme, e.g. speech processing. Moreover, it presents the translation of hidden Markov models’ concepts from the domain of formal mathematics into computer codes using MATLAB. The unique feature of this book is that the theoretical concepts are first presented using an intuition-based approach followed by the description of the fundamental algorithms behind hidden Markov models using MATLAB. This approach, by means of analysis followed by synthesis, is suitable for those who want to study the subject using a more empirical approach.

Key Selling Points: Presents a broad range of concepts related to Hidden Markov Models (HMM), from simple problems to advanced theory + Covers the analysis of both continuous and discrete Markov chains + Discusses the translation of HMM concepts from the realm of formal mathematics into computer code + Offers many examples to supplement mathematical notation when explaining new concepts.

Danny Kopec, Christopher Pileggi, David Ungar, Shweta Shetty ... 350 pages - ISBN-10: 194453458X - ISBN-13: 978-1944534585 ... Publisher : Mercury Learning (June, 2016) - Language: : English.

This book lends insight into solving some well-known AI problems using the most efficient problem-solving methods by humans and computers. The book discusses the importance of developing critical-thinking methods and skills, and develops a consistent approach toward each problem. This book assembles in one place a set of interesting and challenging AI–type problems that students regularly encounter in computer science, mathematics, and AI courses. These problems are not new, and students from all backgrounds can benefit from the kind of deductive thinking that goes into solving them. The book is especially useful as a companion to any course in computer science or mathematics where there are interesting problems to solve.

Features: •Addresses AI and problem-solving from different perspectives •Covers classic AI problems such as Sudoku, Map Coloring, Twelve Coins, Red Donkey, Cryptarithms, Monte Carlo Methods, Rubik’s Cube, Missionaries/Cannibals, Knight’s Tour, Monty Hall, and more •Includes a companion disc with source code, solutions, figures, and more •Offers playability sites where students can exercise the process of developing their solutions •Describes problem-solving methods that might be applied to a variety of situations.

Giuseppe Bonaccorso ... 522 pages - AmazonSIN : B07CSLQGNC - Publisher : Packt Publishing; 2nd Edition (August, 2018) - Language: English.

470 pages - Publisher: Springer; (April, 2020) ... Language: English - AmazonSIN: B086S1JXRW.

This book explains the use of cloud computing systems for engineering applications to satisfy the need for enterprise level, state-of-the-art computational capacities at an affordable cost. As huge costs are involved in the maintenance and timely renovation of computational capabilities, particularly for projects that require significant computational capacity, cloud services can achieve considerable savings for users and organizations engaged in engineering research and development. Dr. Stradi-Granados explains how to extract a maximum value from every dollar invested in cloud computer server. The types of facilities located around the world that lease their resources to customers interested in reducing the internal overhead and implementation time. The volume features chapters on model generation, motion studies, and prototyping is ideal for students, researchers, practitioners, and facility's managers across a range of engineering domains.

Alan Agresti ... 400 pages - Publisher: Wiley; 3rd edition (November, 2018) ... Language: English - ISBN-10: 1119405262 - ISBN-13: 978-1119405269.

A valuable new edition of a standard reference: The use of statistical methods for categorical data has increased dramatically, particularly for applications in the biomedical and social sciences. An Introduction to Categorical Data Analysis, Third Edition summarizes these methods and shows readers how to use them using software. Readers will find a unified generalized linear models approach that connects logistic regression and loglinear models for discrete data with normal regression for continuous data.

Adding to the value in the new edition is: • Illustrations of the use of R software to perform all the analyses in the book • A new chapter on alternative methods for categorical data, including smoothing and regularization methods (such as the lasso), classification methods such as linear discriminant analysis and classification trees, and cluster analysis • New sections in many chapters introducing the Bayesian approach for the methods of that chapter • More than 70 analyses of data sets to illustrate application of the methods, and about 200 exercises, many containing other data sets • An appendix showing how to use SAS, Stata, and SPSS, and an appendix with short solutions to most odd-numbered exercises

Written in an applied, nontechnical style, this book illustrates the methods using a wide variety of real data, including medical clinical trials, environmental questions, drug use by teenagers, horseshoe crab mating, basketball shooting, correlates of happiness, and much more. An Introduction to Categorical Data Analysis, Third Edition is an invaluable tool for statisticians and biostatisticians as well as methodologists in the social and behavioral sciences, medicine and public health, marketing, education, and the biological and agricultural sciences.

Zed Shaw ... 320 pages - Publisher: Addison-Wesley Professional; (June, 2017) ... Language: English - ISBN-10: 0134692888 - ISBN-13: 978-0134692883.

In Learn Python 3 the Hard Way, you’ll learn Python by working through 52 brilliantly crafted exercises. Read them. Type their code precisely. (No copying and pasting!) Fix your mistakes. Watch the programs run. As you do, you’ll learn how a computer works; what good programs look like; and how to read, write, and think about code. Zed then teaches you even more in 5+ hours of video where he shows you how to break, fix, and debug your code—live, as he’s doing the exercises.

Install a complete Python environment + Organize and write code + Fix and break code
Basic mathematics + Variables + Strings and text + Interact with users + Work with files + Looping and logic + Data structures using lists and dictionaries + Program design + Object-oriented programming + Inheritance and composition + Modules, classes, and objects + Python packaging + Automated testing + Basic game development + Basic web development.

It’ll be hard at first. But soon, you’ll just get it—and that will feel great! This course will reward you for every minute you put into it. Soon, you’ll know one of the world’s most powerful, popular programming languages. You’ll be a Python programmer.  This Book Is Perfect For: Total beginners with zero programming experience + Junior developers who know one or two languages + Returning professionals who haven’t written code in years + Seasoned professionals looking for a fast, simple, crash course in Python 3.

Umberto Michelucci ... 431 pages - Publisher: Apress; (September, 2018) ... Language: English - AmazonSIN: B07H6D9NQ8.
Work with advanced topics in deep learning, such as optimization algorithms, hyper-parameter tuning, dropout, and error analysis as well as strategies to address typical problems encountered when training deep neural networks. You’ll begin by studying the activation functions mostly with a single neuron (ReLu, sigmoid, and Swish), seeing how to perform linear and logistic regression using TensorFlow, and choosing the right cost function. The next section talks about more complicated neural network architectures with several layers and neurons and explores the problem of random initialization of weights. An entire chapter is dedicated to a complete overview of neural network error analysis, giving examples of solving problems originating from variance, bias, overfitting, and datasets coming from different distributions. Applied Deep Learning also discusses how to implement logistic regression completely from scratch without using any Python library except NumPy, to let you appreciate how libraries such as TensorFlow allow quick and efficient experiments. Case studies for each method are included to put into practice all theoretical information. You’ll discover tips and tricks for writing optimized Python code (for example vectorizing loops with NumPy).

What You Will Learn: Implement advanced techniques in the right way in Python and TensorFlow + Debug and optimize advanced methods (such as dropout and regularization) + Carry out error analysis (to realize if one has a bias problem, a variance problem, a data offset problem, and so on) + Set up a machine learning project focused on deep learning on a complex dataset. Who This Book Is For: Readers with a medium understanding of machine learning, linear algebra, calculus, and basic Python programming.

Mykel J. Kochenderfer, Tim A. Wheeler ... 520 pages - Publisher: The MIT Press; (March, 2019) ... Language: English - ISBN-10: 0262039427 - ISBN-13: 978-0262039420.

A comprehensive introduction to optimization with a focus on practical algorithms for the design of engineering systems: This book offers a comprehensive introduction to optimization with a focus on practical algorithms. The book approaches optimization from an engineering perspective, where the objective is to design a system that optimizes a set of metrics subject to constraints. Readers will learn about computational approaches for a range of challenges, including searching high-dimensional spaces, handling problems where there are multiple competing objectives, and accommodating uncertainty in the metrics. Figures, examples, and exercises convey the intuition behind the mathematical approaches. The text provides concrete implementations in the Julia programming language. Topics covered include derivatives and their generalization to multiple dimensions; local descent and first- and second-order methods that inform local descent; stochastic methods, which introduce randomness into the optimization process; linear constrained optimization, when both the objective function and the constraints are linear; surrogate models, probabilistic surrogate models, and using probabilistic surrogate models to guide optimization; optimization under uncertainty; uncertainty propagation; expression optimization; and multidisciplinary design optimization. Appendixes offer an introduction to the Julia language, test functions for evaluating algorithm performance, and mathematical concepts used in the derivation and analysis of the optimization methods discussed in the text. The book can be used by advanced undergraduates and graduate students in mathematics, statistics, computer science, any engineering field, (including electrical engineering and aerospace engineering), and operations research, and as a reference for professionals.

Bradley Efron, Trevor Hastie ... 495 pages - Publisher: Cambridge Univ. Press; (July, 2016) ... Language: English - ISBN-10: 1107149894 - ISBN-13: 978-1107149892.

The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.