Statistical Methods for Reliability Data by William Q. Meeker; Luis A. Escobar; Francis G. PascualAn authoritative guide to the most recent advances in statistical methods for quantifying reliability Statistical Methods for Reliability Data, Second Edition (SMRD2) is an essential guide to the most widely used and recently developed statistical methods for reliability data analysis and reliability test planning. Written by three experts in the area, SMRD2 updates and extends the long- established statistical techniques and shows how to apply powerful graphical, numerical, and simulation-based methods to a range of applications in reliability. SMRD2 is a comprehensive resource that describes maximum likelihood and Bayesian methods for solving practical problems that arise in product reliability and similar areas of application. SMRD2 illustrates methods with numerous applications and all the data sets are available on the book's website. Also, SMRD2 contains an extensive collection of exercises that will enhance its use as a course textbook. The SMRD2's website contains valuable resources, including R packages, Stan model codes, presentation slides, technical notes, information about commercial software for reliability data analysis, and csv files for the 93 data sets used in the book's examples and exercises. The importance of statistical methods in the area of engineering reliability continues to grow and SMRD2 offers an updated guide for, exploring, modeling, and drawing conclusions from reliability data. SMRD2 features: Contains a wealth of information on modern methods and techniques for reliability data analysis Offers discussions on the practical problem-solving power of various Bayesian inference methods Provides examples of Bayesian data analysis performed using the R interface to the Stan system based on Stan models that are available on the book's website Includes helpful technical-problem and data-analysis exercise sets at the end of every chapter Presents illustrative computer graphics that highlight data, results of analyses, and technical concepts Written for engineers and statisticians in industry and academia, Statistical Methods for Reliability Data, Second Edition offers an authoritative guide to this important topic.
Publication Date: 2022
The Python Book by Rob MastrodomenicoThe Python Book Discover the power of one of the fastest growing programming languages in the world with this insightful new resource The Python Book delivers an essential introductory guide to learning Python for anyone who works with data but does not have experience in programming. The author, an experienced data scientist and Python programmer, shows readers how to use Python for data analysis, exploration, cleaning, and wrangling. Readers will learn what in the Python language is important for data analysis, and why. The Python Book offers readers a thorough and comprehensive introduction to Python that is both simple enough to be ideal for a novice programmer, yet robust to be useful for those more experienced in the language. The book assists budding programmers to gradually increase their skills as they move through the book, always with an understanding of what they are covering and why it is useful. Used by major companies like Google, Facebook, Instagram, Spotify, and more, Python promises to remain central to the programming landscape for years to come. Containing a thorough discussion of Python programming topics like variables, equalities and comparisons, tuple and dictionary data types, while and for loops, and if statements, readers will also learn: How to use highly useful Python programming libraries, including Pandas and Matplotlib How to write Python functions and classes How to write and use Python scripts To deal with different data types within Python Perfect for statisticians, computer scientists, software programmers, and practitioners working in private industry and medicine, The Python Book will also be of interest to students in any of the aforementioned fields. As it assumes no programming experience or knowledge, the book is ideal for those who work with data and want to learn to use Python to enhance their work.
Publication Date: 2022
Introduction to Algebraic Coding Theory by T. T. Moh"In this age of technology where messages are transmitted in sequences of 0s and 1s through space, errors can occur due to noisy channels. Thus, self-correcting code is vital to eradicate these errors when the number of errors is small. It is widely used in industry for a variety of applications including e-mail, telephone, and remote sensing (for example, photographs of Mars). An expert in algebra and algebraic geometry, Tzuong-Tsieng Moh covers many essential aspects of algebraic coding theory in this book, such as elementary algebraic coding theories, the mathematical theory of vector spaces and linear algebras behind them, various rings and associated coding theories, a fast decoding method, useful parts of algebraic geometry and geometric coding theories. This book is accessible to advanced college students, graduate students, coding theorists and algebraic geometers"--
Publication Date: 2022
Combinatorial Mathematics by Douglas B. WestThis long-awaited textbook is the most comprehensive introduction to a broad swath of combinatorial and discrete mathematics. The text covers enumeration, graphs, sets, and methods, and it includes both classical results and more recent developments. Assuming no prior exposure to combinatorics, it explains the basic material for graduate-level students in mathematics and computer science. Optional more advanced material also makes it valuable as a research reference. Suitable for a one-year course or a one-semester introduction, this textbook prepares students to move on to more advanced material. It is organized to emphasize connections among the topics, and facilitate instruction, self-study, and research, with more than 2200 exercises (many accompanied by hints) at various levels of difficulty. Consistent notation and terminology are used throughout, allowing for a discussion of diverse topics in a unified language. The thorough bibliography, containing thousands of citations, makes this a valuable source for students and researchers alike.
Publication Date: 2021
Computer Vision Using Deep Learning by Vaibhav VerdhanOrganizations spend huge resources in developing software that can perform the way a human does. Image classification, object detection and tracking, pose estimation, facial recognition, and sentiment estimation all play a major role in solving computer vision problems. This book will bring into focus these and other deep learning architectures and techniques to help you create solutions using Keras and the TensorFlow library. You'll also review mutliple neural network architectures, including LeNet, AlexNet, VGG, Inception, R-CNN, Fast R-CNN, Faster R-CNN, Mask R-CNN, YOLO, and SqueezeNet and see how they work alongside Python code via best practices, tips, tricks, shortcuts, and pitfalls. All code snippets will be broken down and discussed thoroughly so you can implement the same principles in your respective environments. Computer Vision Using Deep Learning offers a comprehensive yet succinct guide that stitches DL and CV together to automate operations, reduce human intervention, increase capability, and cut the costs. What You'll Learn Examine deep learning code and concepts to apply guiding principals to your own projects Classify and evaluate various architectures to better understand your options in various use cases Go behind the scenes of basic deep learning functions to find out how they work Who This Book Is For Professional practitioners working in the fields of software engineering and data science. A working knowledge of Python is strongly recommended. Students and innovators working on advanced degrees in areas related to computer vision and Deep Learning.