In " An Introduction to the Event-Related Potential Technique," Steve Luck offers the first comprehensive guide to the practicalities of conducting ERP experiments in cognitive neuroscience and related fields, including affective neuroscience and experimental psychopathology. The book can serve as a guide for the classroom or the laboratory and as a reference for researchers who do not conduct ERP studies themselves but need to understand and evaluate ERP experiments in the literature. It summarizes the accumulated body of ERP theory and practice, providing detailed, practical advice about how to design, conduct, and interpret ERP experiments, and presents the theoretical background needed to understand why an experiment is carried out in a particular way. Luck focuses on the most fundamental techniques, describing them as they are used in many of the world's leading ERP laboratories. These techniques reflect a long history of electrophysiological recordings and provide an excellent foundation for more advanced approaches.The book also provides advice on the key topic of how to design ERP experiments so that they will be useful in answering questions of broad scientific interest. This reflects the increasing proportion of ERP research that focuses on these broader questions rather than the "ERPology" of early studies, which concentrated primarily on ERP components and methods. Topics covered include the neural origins of ERPs, signal averaging, artifact rejection and correction, filtering, measurement and analysis, localization, and the practicalities of setting up the lab.

    This title is intended for those who might enjoy an engaging dialogue on abstract mathematical ideas, and those who might wish to experience how new mathematics is created.

    Written in a concise, bulleted outline format, this well-illustrated text offers 500 USMLE-style review questions, answers, and explanations and features comprehensive content and upgraded USMLE Step 1 information.

    A step-by-step visual journey through the mathematics of neural networks, and making your own using Python and Tensorflow.

    With this insightful book, intermediate to experienced programmers interested in data analysis will learn techniques for working with data in a business environment. You'll learn how to look at data to discover what it contains, how to capture those ideas in conceptual models, and then feed your understanding back into the organization through business plans, metrics dashboards, and other applications.Along the way, you'll experiment with concepts through hands-on workshops at the end of each chapter. Above all, you'll learn how to think about the results you want to achieve -- rather than rely on tools to think for you.Use graphics to describe data with one, two, or dozens of variablesDevelop conceptual models using back-of-the-envelope calculations, as well asscaling and probability argumentsMine data with computationally intensive methods such as simulation and clusteringMake your conclusions understandable through reports, dashboards, and other metrics programsUnderstand financial calculations, including the time-value of moneyUse dimensionality reduction techniques or predictive analytics to conquer challenging data analysis situationsBecome familiar with different open source programming environments for data analysisFinally, a concise reference for understanding how to conquer piles of data.--Austin King, Senior Web Developer, MozillaAn indispensable text for aspiring data scientists.--Michael E. Driscoll, CEO/Founder, Dataspora

    This book focuses on practical algorithms that have been used to solve key problems in data mining and which can be used on even the largest datasets. It begins with a discussion of the map-reduce framework, an important tool for parallelizing algorithms automatically. The authors explain the tricks of locality-sensitive hashing and stream processing algorithms for mining data that arrives too fast for exhaustive processing. The PageRank idea and related tricks for organizing the Web are covered next. Other chapters cover the problems of finding frequent itemsets and clustering. The final chapters cover two applications: recommendation systems and Web advertising, each vital in e-commerce. Written by two authorities in database and Web technologies, this book is essential reading for students and practitioners alike.

    Mind Children, written by an internationally renowned roboticist, offers a comparable experience--a mind-boggling glimpse of a world we may soon share with our artificial progeny. Filled with fresh ideas and insights, this book is one of the most engaging and controversial visions of the future ever written by a serious scholar.Hans Moravec convincingly argues that we are approaching a watershed in the history of life--a time when the boundaries between biological and postbiological intelligence will begin to dissolve. Within forty years, Moravec believes, we will achieve human equivalence in our machines, not only in their capacity to reason but also in their ability to perceive, interact with, and change their complex environment. The critical factor is mobility. A computer rooted to one place is doomed to static iterations, whereas a machine on the prowl, like a mobile organism, must evolve a richer fund of knowledge about an ever-changing world upon which to base its actions.In order to achieve anything near human equivalence, robots will need, at the least, the capacity to perform ten trillion calculations per second. Given the trillion-fold increase in computational power since the end of the nineteenth century, and the promise of exotic technologies far surpassing the now-familiar lasers and even superconductors, Moravec concludes that our hardware will have no trouble meeting this forty-year timetable.But human equivalence is just the beginning, not an upper bound. Once the tireless thinking capacity of robots is directed to the problem of their own improvement and reproduction, even the sky will not limit their voracious exploration of the universe. In the concluding chapters Moravec challenges us to imagine with him the possibilities and pitfalls of such a scenario. Rather than warning us of takeover by robots, the author invites us, as we approach the end of this millennium, to speculate about a plausible, wonderful postbiological future and the ways in which our minds might participate in its unfolding.

    

    But with the increasing power of visualization software today, scientists, engineers, and business analysts often have to navigate a bewildering array of visualization choices and options.This practical book takes you through many commonly encountered visualization problems, and it provides guidelines on how to turn large datasets into clear and compelling figures. What visualization type is best for the story you want to tell? How do you make informative figures that are visually pleasing? Author Claus O. Wilke teaches you the elements most critical to successful data visualization.Explore the basic concepts of color as a tool to highlight, distinguish, or represent a valueUnderstand the importance of redundant coding to ensure you provide key information in multiple waysUse the book's visualizations directory, a graphical guide to commonly used types of data visualizationsGet extensive examples of good and bad figuresLearn how to use figures in a document or report and how employ them effectively to tell a compelling story

    It was not until moments before landing that anything went wrong. Coming in to Heathrow Airport, the plane suffered inexplicable loss of power to both engines, and it was suddenly likely that the plane would plough into a built-up area outside the airport, with the loss of all lives on board. Peter tells us in graphic detail his thoughts and actions when he managed to help save the plane at the last moment thanks to a flash of inspiration that led him to change the position of the wing flaps, which appeared to gain the vehicle enough precious time to make it over the perimeter fence and land on the grass, short of the runway. For both Maria and Peter, their lives following the crash have resulted in experiences that they never would have expected to have happened. There isn't a handbook with rules to follow after a crash so the subsequent aftermath was laced with events that could have been handled better from all sides, which lead to Maria and Peter having to find strength inside them that they had never needed before. A little more than a year later, they have used these strengths to begin a new chapter in their lives; starting with leaving British Airways and celebrating a second chance to enjoy life.But there are still nights when they find themselves awake, crying about what could have happened on that fateful day.

    Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications.Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.

    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.

    Non- mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics. Dr. Kuhn is a Director of Non-Clinical Statistics at Pfizer Global R&D in Groton Connecticut. He has been applying predictive models in the pharmaceutical and diagnostic industries for over 15 years and is the author of a number of R packages. Dr. Johnson has more than a decade of statistical consulting and predictive modeling experience in pharmaceutical research and development. He is a co-founder of Arbor Analytics, a firm specializing in predictive modeling and is a former Director of Statistics at Pfizer Global R&D. His scholarly work centers on the application and development of statistical methodology and learning algorithms. Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. Addressing practical concerns extends beyond model fitting to topics such as handling class imbalance, selecting predictors, and pinpointing causes of poor model performance-all of which are problems that occur frequently in practice. The text illustrates all parts of the modeling process through many hands-on, real-life examples. And every chapter contains extensive R code f

    Cases, datasets, and examples allow for a more real-world perspective and explore relevant uses of regression techniques in business today.

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