Wolfram lets the world see his work in A New Kind of Science, a gorgeous, 1,280-page tome more than a decade in the making. With patience, insight, and self-confidence to spare, Wolfram outlines a fundamental new way of modeling complex systems. On the frontier of complexity science since he was a boy, Wolfram is a champion of cellular automata--256 "programs" governed by simple nonmathematical rules. He points out that even the most complex equations fail to accurately model biological systems, but the simplest cellular automata can produce results straight out of nature--tree branches, stream eddies, and leopard spots, for instance. The graphics in A New Kind of Science show striking resemblance to the patterns we see in nature every day. Wolfram wrote the book in a distinct style meant to make it easy to read, even for nontechies; a basic familiarity with logic is helpful but not essential. Readers will find themselves swept away by the elegant simplicity of Wolfram's ideas and the accidental artistry of the cellular automaton models. Whether or not Wolfram's revolution ultimately gives us the keys to the universe, his new science is absolutely awe-inspiring. --Therese Littleton

    Rothman's acclaimed Modern Epidemiology reflects the remarkable conceptual development of this evolving science and the engagement of epidemiologists with an increasing range of current public health concerns. This landmark work is the most comprehensive and cohesive text on the principles and methods of contemporary epidemiologic research.Coauthored by two leading epidemiologists, with 15 additional contributors, the Second Edition presents a much broader range of concepts and methods than Dr. Rothman's single-authored original edition. Coverage of basic measures and study types is more thorough and includes a new chapter on field methods. New chapters on advanced topics in data analysis, such as hierarchical regression, are also included. A new section covers specific areas of research such as infectious disease epidemiology, ecologic studies, disease surveillance, analysis of vital statistics, screening, clinical epidemiology, environmental and occupational epidemiology, reproductive and perinatal epidemiology, genetic epidemiology, and nutritional epidemiology.

     Second edition of a popular introductory textbook, combining breadth of coverage, practical examples, and student-friendly features Includes new sections on metaphor, framing, stance and style, multimodal discourse, and Gricean pragmatics Considers a variety of approaches to the subject, including critical discourse analysis, conversation analysis, interactional and variationist sociolinguistics, ethnography, corpus linguistics, and other qualitative and quantitative methods Features detailed descriptions of the results of discourse analysts' work Retains and expands the useful student features, including discussion questions, exercises, and ideas for small research projects.

    With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication.Packed with examples and exercises, Natural Language Processing with Python will help you: Extract information from unstructured text, either to guess the topic or identify "named entities" Analyze linguistic structure in text, including parsing and semantic analysis Access popular linguistic databases, including WordNet and treebanks Integrate techniques drawn from fields as diverse as linguistics and artificial intelligenceThis book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful.

    You want to learn HTML so you can finally create those web pages you've always wanted, so you can communicate more effectively with friends, family, fans, and fanatic customers. You also want to do it right so you can actually maintain and expand your web pages over time so they work in all browsers and mobile devices. Oh, and if you've never heard of CSS, that's okay--we won't tell anyone you're still partying like it's 1999--but if you're going to create web pages in the 21st century then you'll want to know and understand CSS. Learn the real secrets of creating web pages, and why everything your boss told you about HTML tables is probably wrong (and what to do instead). Most importantly, hold your own with your co-worker (and impress cocktail party guests) when he casually mentions how his HTML is now strict, and his CSS is in an external style sheet. With Head First HTML, you'll avoid the embarrassment of thinking web-safe colors still matter, and the foolishness of slipping a font tag into your pages. Best of all, you'll learn HTML and CSS in a way that won't put you to sleep. If you've read a Head First book, you know what to expect: a visually-rich format designed for the way your brain works. Using the latest research in neurobiology, cognitive science, and learning theory, this book will load HTML and CSS into your brain in a way that sticks. So what are you waiting for? Leave those other dusty books behind and come join us in Webville. Your tour is about to begin.

    Sebi is who you need. Dr. Sebi was a Honduran herbalist and healer who discovered that a simple diet could be the cure for so many illnesses in the world. Think about the number of auto-immune diseases there are, such as HIV and lupus. Doctors don’t know how to heal those diseases. All that is available are medicines to help control them, which is great, but wouldn’t it be great if there was something you could do that would get rid of the disease altogether? Dr. Sebi wanted that, and that’s what he did.Within these pages, you will learn: How Dr. Sebi’s treatment plan can help STDs like herpes and HIV The reason why eliminating mucus can help with diabetes Why hair loss no longer has to be permanent Dr. Sebi’s nutritional guide Who Dr. Sebi is and his treatment philosophy … And much more. Understand that this may seem all too good to be true, or that it’s telling you modern medicine is completely bad. You will find that Dr. Sebi never tells you to stop taking medicines prescribe by doctors. Instead, you will use his diet, products, and treatments, along with your doctor’s orders to help you heal. Right now, it’s up to you to make the final decision. Stay exactly as you are right now. Fed up with how you feel, and unable to do anything about it, or buy this book and make changes your body will love you for. Go ahead, scroll back up and click “Buy now.”

    

    

    The lessons are based on realistic conversations, and task-based listening sections consolidate language acquisition.

    This second edition is appropriate as a supplementary (companion) text for any college-level course covering digital signal processing.

    All chapters are supplemented by thought-provoking problems. A useful work for graduate-level students with backgrounds in computer science, operations research, and electrical engineering. "Mathematicians wishing a self-contained introduction need look no further." — American Mathematical Monthly.

    A computational investigation into the human representation and processing of visual information.

    Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation.Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you'll learn how to analyze sample datasets and write simple machine learning algorithms. "Machine Learning for Hackers" is ideal for programmers from any background, including business, government, and academic research.Develop a naive Bayesian classifier to determine if an email is spam, based only on its textUse linear regression to predict the number of page views for the top 1,000 websitesLearn optimization techniques by attempting to break a simple letter cipherCompare and contrast U.S. Senators statistically, based on their voting recordsBuild a "whom to follow" recommendation system from Twitter data

    

    It is the technology behind photo tagging systems at Facebook and Google, self-driving cars, speech recognition systems on your smartphone, and much more.In particular, Deep learning excels at solving machine perception problems: understanding the content of image data, video data, or sound data. Here's a simple example: say you have a large collection of images, and that you want tags associated with each image, for example, "dog," "cat," etc. Deep learning can allow you to create a system that understands how to map such tags to images, learning only from examples. This system can then be applied to new images, automating the task of photo tagging. A deep learning model only has to be fed examples of a task to start generating useful results on new data.