The Clockwork Muse is designed to help prospective authors develop a workable timetable for completing long and often formidable projects.The idea of dashing off a manuscript in a fit of manic inspiration may be romantic, but it is not particularly practical. Instead, Eviatar Zerubavel, a prolific and successful author, describes how to set up a writing schedule and regular work habits that will take most of the anxiety and procrastination out of long-term writing, and even make it enjoyable. The dreaded 'writer's block' often turns out to be simply a need for a better grasp of the temporal organization of work.The Clockwork Muse rethinks the writing process in terms of time and organization. It offers writers a simple yet comprehensive framework that considers such variables as when to write, for how long, and how often, while keeping a sense of momentum throughout the entire project. It shows how to set priorities, balance ideals against constraints, and find the ideal time to write. For all those whose writing has languished, waiting for the "right moment," The Clockwork Muse announces that the moment has arrived.

    It can be freely downloaded and it works on multiple computer platforms. This book provides an elementary introduction to R. In each chapter, brief introductory sections are followed by code examples and comments from the computational and statistical viewpoint. A supplementary R package containing the datasets can be downloaded from the web.

    The main ideas and applications of the metallurgy are provided in this book.

    It is universally recognized as the standard English version of Aristotle. This revised edition contains the substance of the original translation, slightly emended in light of recent scholarship; three of the original versions have been replaced by new translations; and a new and enlarged selection of Fragments has been added. The aim of the translation remains the same: to make the surviving works of Aristotle readily accessible to English speaking readers.

    

    But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like nonparametric curve estimation, bootstrapping, and clas- sification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analyzing data. For some time, statistics research was con- ducted in statistics departments while data mining and machine learning re- search was conducted in computer science departments. Statisticians thought that computer scientists were reinventing the wheel. Computer scientists thought that statistical theory didn't apply to their problems. Things are changing. Statisticians now recognize that computer scientists are making novel contributions while computer scientists now recognize the generality of statistical theory and methodology. Clever data mining algo- rithms are more scalable than statisticians ever thought possible. Formal sta- tistical theory is more pervasive than computer scientists had realized.

    DLC: Quantum theory.

    The field is ready for a text that not only demonstrates how to use the algorithms that make up machine learning methods, but also provides the background needed to understand how and why these algorithms work. Machine Learning: An Algorithmic Perspective is that text.Theory Backed up by Practical ExamplesThe book covers neural networks, graphical models, reinforcement learning, evolutionary algorithms, dimensionality reduction methods, and the important area of optimization. It treads the fine line between adequate academic rigor and overwhelming students with equations and mathematical concepts. The author addresses the topics in a practical way while providing complete information and references where other expositions can be found. He includes examples based on widely available datasets and practical and theoretical problems to test understanding and application of the material. The book describes algorithms with code examples backed up by a website that provides working implementations in Python. The author uses data from a variety of applications to demonstrate the methods and includes practical problems for students to solve.Highlights a Range of Disciplines and ApplicationsDrawing from computer science, statistics, mathematics, and engineering, the multidisciplinary nature of machine learning is underscored by its applicability to areas ranging from finance to biology and medicine to physics and chemistry. Written in an easily accessible style, this book bridges the gaps between disciplines, providing the ideal blend of theory and practical, applicable knowledge."

    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 Cameron Davidson-Pilon has worked in many areas of applied mathematics, from the evolutionary dynamics of genes and diseases to stochastic modeling of financial prices. His contributions to the open source community include lifelines, an implementation of survival analysis in Python. Educated at the University of Waterloo and at the Independent University of Moscow, he currently works with the online commerce leader Shopify.

    This text enables students to conduct serious analysis of applied problems in contrast to merely running simple“canned” applications to help students become stronger analysts and future managers. It is also at a mathematically higher level than most business statistics texts.

    These topics lie at the heart of many exciting areas of contemporary science and engineering - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics, and cryptography. This textbook introduces theory in tandem with applications. Information theory is taught alongside practical communication systems, such as arithmetic coding for data compression and sparse-graph codes for error-correction. A toolbox of inference techniques, including message-passing algorithms, Monte Carlo methods, and variational approximations, are developed alongside applications of these tools to clustering, convolutional codes, independent component analysis, and neural networks. The final part of the book describes the state of the art in error-correcting codes, including low-density parity-check codes, turbo codes, and digital fountain codes -- the twenty-first century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, David MacKay's groundbreaking book is ideal for self-learning and for undergraduate or graduate courses. Interludes on crosswords, evolution, and sex provide entertainment along the way. In sum, this is a textbook on information, communication, and coding for a new generation of students, and an unparalleled entry point into these subjects for professionals in areas as diverse as computational biology, financial engineering, and machine learning.

    These include teachers, social workers and health service professionals, managers and specialists in business, architects, designers, criminologists and accountants among many others.Real World Research provides a clear route-map of the various steps needed to carry out a piece of applied research to a high professional standard. It is accessible to those without a social science background while providing rigorous and fully up-to-date coverage of contemporary issues and debates. It brings together materials and approaches from different social science disciplines, seeing value in both quantitative and qualitative approaches, as well as their combination in mixed-method designs.

    From students with disabilities, culturally diverse students, and students with limited English proficiency to economically disadvantaged students this text provides teachers with the tools they need in their diverse classrooms. Revised to reflect the most current research, terminology and teaching practices, the strength of this text continues to be its numerous learning activities and sample lessons addressing both elementary and secondary classrooms. This edition continues its very popular multi- chapter unit on curriculum adaptations with specific strategies and activities for teaching reading, writing, and mathematics. With a new chapter on Response to Intervention and Progress Monitoring and full integration of the RTI framework, and the increase emphasis on middle and secondary students, this text continues its reign as an outstanding resource for all general education teachers. 0131381253 / 9780131381254 Teaching Students Who are Exceptional, Diverse, and at Risk in the General Education Classroom (with MyEducationLab) Package consists of 0135140870 / 9780135140871 MyEducationLab -- Access Card 0137151799 / 9780137151790 Teaching Students Who are Exceptional, Diverse, and at Risk in the General Education Classroom

    Where do you look for information when transferring a patient from one drug to another? Where do you find a clear overview when dealing with a complex patient (e.g, with co-morbid epilepsy or liver disease or HIV infection)? Where can you seek advice on prescribing psychotropics during pregnancy? "The Maudsley Prescribing Guidelines in Psychiatry"! The leading clinical reference for handling prescribing problems as encountered in daily practice and for formulating prescribing policy.Evidence-based and written by expertsThis book is the essential guide for anyone responsible for prescribing, dispensing or administering drugs for patients with mental health disorders. All the evidence has been reviewed and summarized succinctly by an expert team of psychiatrists and pharmacists.New content and improved formatThis new edition makes greater use of tables and boxes to facilitate quick reference and includes new sections on cytochrome-mediated interactions and psychiatric side effects of non-psychotropic drugs.Clinically relevantChapters address plasma monitoring, schizophrenia, bipolar disorder, depression and anxiety, children and adolescents, substance abuse and special patient groups. Each section has a full reference list. The book covers prescribing drugs outside their licensed indications and their interaction with substances such as alcohol, nicotine and caffeine.Useful for all levels of experienceTrainees will gain important information regarding the rational, safe and effective use of medications for patients with mental illness. Experienced clinicians will find excellent guidance regarding more complex issues that they may not encounter regularly.Why the Maudsley Prescribing Guidelines in Psychiatry?Long recognized as an international trailblazer in mental health care, the Maudsley Hospital earned its reputation for excellence in both in-patient and community care. It is highly regarded for its research, and pioneered the use of clinical neuroscience. You can trust "The Maudsley Prescribing Guidelines in Psychiatry" to be scientifically sound and clinically effective.

    By investing some of the lesser-known corners of psychology, neuroscience, physics, history, and even crime, all through the lens of trickery and illusion, Fooling Houdini arrives at a host of startling revelations about how the mind works--and why, sometimes, it doesn’t.