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The must-read summary of Vivek Ranadive and Kevin Maney's book: "The Two-Second Advantage: How We Succeed by Anticipating the Future". This complete summary of the ideas from Vivek Ranadive and Kevin Maney's book "The Two-Second Advantage" shows that the challenge of the future is to become better at predictive analysis. This summary highlights that if you can combine the right information at the right time and in the right context just far enough ahead, you have all the ingredients for success. Added-value of this summary: • Save time • Understand key concepts • Expand your knowledge To learn more, read "The Two-Second Advantage" and become better at predictive analysis through a blend of talented brains and talented systems.
There is a competitive advantage out there, arguably more powerful than any other. Is it superior strategy? Faster innovation? Smarter employees? No, New York Times best-selling author, Patrick Lencioni, argues that the seminal difference between successful companies and mediocre ones has little to do with what they know and how smart they are and more to do with how healthy they are. In this book, Lencioni brings together his vast experience and many of the themes cultivated in his other best-selling books and delivers a first: a cohesive and comprehensive exploration of the unique advantage organizational health provides. Simply put, an organization is healthy when it is whole, consistent and complete, when its management, operations and culture are unified. Healthy organizations outperform their counterparts, are free of politics and confusion and provide an environment where star performers never want to leave. Lencioni’s first non-fiction book provides leaders with a groundbreaking, approachable model for achieving organizational health—complete with stories, tips and anecdotes from his experiences consulting to some of the nation’s leading organizations. In this age of informational ubiquity and nano-second change, it is no longer enough to build a competitive advantage based on intelligence alone. The Advantage provides a foundational construct for conducting business in a new way—one that maximizes human potential and aligns the organization around a common set of principles.
The winner of the UK's Business Book of the Year Award for 2021, this is a groundbreaking exposé of the myths behind startup success and a blueprint for harnessing the things that really matter. What is the difference between a startup that makes it, and one that crashes and burns? Behind every story of success is an unfair advantage. But an Unfair Advantage is not just about your parents' wealth or who you know: anyone can have one. An Unfair Advantage is the element that gives you an edge over your competition. This groundbreaking book shows how to identify your own Unfair Advantages and apply them to any project. Drawing on over two decades of hands-on experience, Ash Ali and Hasan Kubba offer a unique framework for assessing your external circumstances in addition to your internal strengths. Hard work and grit aren't enough, so they explore the importance of money, intelligence, location, education, expertise, status, and luck in the journey to success. From starting your company, to gaining traction, raising funds, and growth hacking, The Unfair Advantage helps you look at yourself and find the ingredients you didn't realize you already had, to succeed in the cut-throat world of business.
This book presents advanced methodologies in two areas related to electroencephalogram (EEG) signals: detection of epileptic seizures and identification of mental states in brain computer interface (BCI) systems. The proposed methods enable the extraction of this vital information from EEG signals in order to accurately detect abnormalities revealed by the EEG. New methods will relieve the time-consuming and error-prone practices that are currently in use. Common signal processing methodologies include wavelet transformation and Fourier transformation, but these methods are not capable of managing the size of EEG data. Addressing the issue, this book examines new EEG signal analysis approaches with a combination of statistical techniques (e.g. random sampling, optimum allocation) and machine learning methods. The developed methods provide better results than the existing methods. The book also offers applications of the developed methodologies that have been tested on several real-time benchmark databases. This book concludes with thoughts on the future of the field and anticipated research challenges. It gives new direction to the field of analysis and classification of EEG signals through these more efficient methodologies. Researchers and experts will benefit from its suggested improvements to the current computer-aided based diagnostic systems for the precise analysis and management of EEG signals. /div
Incorporating new and updated information, this second edition of THE bestselling text in Bayesian data analysis continues to emphasize practice over theory, describing how to conceptualize, perform, and critique statistical analyses from a Bayesian perspective. Its world-class authors provide guidance on all aspects of Bayesian data analysis and include examples of real statistical analyses, based on their own research, that demonstrate how to solve complicated problems. Changes in the new edition include: Stronger focus on MCMC Revision of the computational advice in Part III New chapters on nonlinear models and decision analysis Several additional applied examples from the authors' recent research Additional chapters on current models for Bayesian data analysis such as nonlinear models, generalized linear mixed models, and more Reorganization of chapters 6 and 7 on model checking and data collection Bayesian computation is currently at a stage where there are many reasonable ways to compute any given posterior distribution. However, the best approach is not always clear ahead of time. Reflecting this, the new edition offers a more pluralistic presentation, giving advice on performing computations from many perspectives while making clear the importance of being aware that there are different ways to implement any given iterative simulation computation. The new approach, additional examples, and updated information make Bayesian Data Analysis an excellent introductory text and a reference that working scientists will use throughout their professional life.
This book offers readers an essential introduction to the fundamentals of digital image processing. Pursuing a signal processing and algorithmic approach, it makes the fundamentals of digital image processing accessible and easy to learn. It is written in a clear and concise manner with a large number of 4 x 4 and 8 x 8 examples, figures and detailed explanations. Each concept is developed from the basic principles and described in detail with equal emphasis on theory and practice. The book is accompanied by a companion website that provides several MATLAB programs for the implementation of image processing algorithms. The book also offers comprehensive coverage of the following topics: Enhancement, Transform processing, Restoration, Registration, Reconstruction from projections, Morphological image processing, Edge detection, Object representation and classification, Compression, and Color processing.
Behavior Analysis with Machine Learning Using R introduces machine learning and deep learning concepts and algorithms applied to a diverse set of behavior analysis problems. It focuses on the practical aspects of solving such problems based on data collected from sensors or stored in electronic records. The included examples demonstrate how to perform common data analysis tasks such as: data exploration, visualization, preprocessing, data representation, model training and evaluation. All of this, using the R programming language and real-life behavioral data. Even though the examples focus on behavior analysis tasks, the covered underlying concepts and methods can be applied in any other domain. No prior knowledge in machine learning is assumed. Basic experience with R and basic knowledge in statistics and high school level mathematics are beneficial. Features: Build supervised machine learning models to predict indoor locations based on WiFi signals, recognize physical activities from smartphone sensors and 3D skeleton data, detect hand gestures from accelerometer signals, and so on. Program your own ensemble learning methods and use Multi-View Stacking to fuse signals from heterogeneous data sources. Use unsupervised learning algorithms to discover criminal behavioral patterns. Build deep learning neural networks with TensorFlow and Keras to classify muscle activity from electromyography signals and Convolutional Neural Networks to detect smiles in images. Evaluate the performance of your models in traditional and multi-user settings. Build anomaly detection models such as Isolation Forests and autoencoders to detect abnormal fish behaviors. This book is intended for undergraduate/graduate students and researchers from ubiquitous computing, behavioral ecology, psychology, e-health, and other disciplines who want to learn the basics of machine learning and deep learning and for the more experienced individuals who want to apply machine learning to analyze behavioral data.
This book covers several of the statistical concepts and data analytic skills needed to succeed in data-driven life science research. The authors proceed from relatively basic concepts related to computed p-values to advanced topics related to analyzing highthroughput data. They include the R code that performs this analysis and connect the lines of code to the statistical and mathematical concepts explained.
This book provides the reader with empirical findings on innovative signal processing approaches to detecting pathologies in infant cries, by comparing new technological approaches to standard ones. The contributors examine novel approaches to machine adaptation to dysarthric speech.
This title is no longer stocked by us. It is now available directly from Christopher Enright: [email protected] How should lawyers go about their tasks in working with law, in making, interpreting, using, reading and writing law? Enright's book describes clear and simple techniques for working with law. It explains why the technique is needed and what it achieves, and then provides a model for doing it. Each model consists of a step by step guide for performing the relevant task. Legal Technique is structured to be the textbook in an introductory law course where the techniques are described, and intended for re-use in later courses on substantive law where these techniques must be further taught and practised in the context of those subjects. Legal Technique is accompanied by a free Legal Technique eWorkbook (see Supplement) containing materials, questions and answers. Included are exercises for working with statutes, cases, legal texts and for solving legal problems; further exercises to practise approaches to common law and statutory law subjects generally; and specific exercises for the subjects 'Introduction to Law', 'Constitutional Law', and 'Property Law'.