Truth and error are interdependent; claims to truth can be made only in the light of previous error. In The Necessity of Errors, John Roberts explores how, up to Hegel, emphasis was placed on error as something that dissolves truth and needs to be eradicated. Drawing on the fragmented corpus of writing on error, from Locke to Luxemburg, Adorno to Vaneigem, and covering five key areas from philosophy to political praxis, this wide-ranging account explores how we learn from error, under what conditions, and with what means. Errors, Roberts finds, are productive, but not in any uniform sense or under all circumstances-a theory of errors needs a dialectics of error.

The aim of this book is to discuss the need for a uniform contract law in Europe. At present it is debated to what extent uniformity of law is required from the economic perspective. The view of the European Commission seems to be that diversity of law stands in the way of a proper functioning of the internal market, but this view does not seem to be shared by business: in the reactions to the 'Communication on European Contract Law (2001), it was striking to see that most companies do not consider the present diversity to be a true barrier to trade. This book offers five different perspectives on the need for a uniform contract law. These perspectives include economics, behavioral law and economics, psychology and law.

Interdisciplinary approaches to identifying, understanding, and remediating people's reliance on inaccurate information that they should know to be wrong. Our lives revolve around the acquisition of information. Sometimes the information we acquire—from other people, from books, or from the media—is wrong. Studies show that people rely on such misinformation, sometimes even when they are aware that the information is inaccurate or invalid. And yet investigations of learning and knowledge acquisition largely ignore encounters with this sort of problematic material. This volume fills the gap, offering theoretical and empirical perspectives on the processing of misinformation and its consequences. The contributors, from cognitive science and education science, provide analyses that represent a variety of methodologies, theoretical orientations, and fields of expertise. The chapters describe the behavioral consequences of relying on misinformation and outline possible remediations; discuss the cognitive activities that underlie encounters with inaccuracies, investigating why reliance occurs so readily; present theoretical and philosophical considerations of the nature of inaccuracies; and offer formal, empirically driven frameworks that detail when and how inaccuracies will lead to comprehension difficulties. Contributors Peter Afflerbach, Patricia A. Alexander, Jessica J. Andrews, Peter Baggetta, Jason L. G. Braasch, Ivar Bråten, M. Anne Britt, Rainer Bromme, Luke A. Buckland, Clark A. Chinn, Byeong-Young Cho, Sidney K. D'Mello, Andrea A. diSessa, Ullrich K. H. Ecker, Arthur C. Graesser, Douglas J. Hacker, Brenda Hannon, Xiangen Hu, Maj-Britt Isberner, Koto Ishiwa, Matthew E. Jacovina, Panayiota Kendeou, Jong-Yun Kim, Stephan Lewandowsky, Elizabeth J. Marsh, Ruth Mayo, Keith K. Millis, Edward J. O'Brien, Herre van Oostendorp, José Otero, David N. Rapp, Tobias Richter, Ronald W. Rinehart, Yaacov Schul, Colleen M. Seifert, Marc Stadtler, Brent Steffens, Helge I. Strømsø, Briony Swire, Sharda Umanath

Become a successful machine learning professional by effortlessly deploying machine learning models to production and implementing cloud-based machine learning models for widespread organizational use Key FeaturesLearn best practices about bringing your models to productionExplore the tools available for serving ML models and the differences between themUnderstand state-of-the-art monitoring approaches for model serving implementationsBook Description Serving patterns enable data science and ML teams to bring their models to production. Most ML models are not deployed for consumers, so ML engineers need to know the critical steps for how to serve an ML model. This book will cover the whole process, from the basic concepts like stateful and stateless serving to the advantages and challenges of each. Batch, real-time, and continuous model serving techniques will also be covered in detail. Later chapters will give detailed examples of keyed prediction techniques and ensemble patterns. Valuable associated technologies like TensorFlow severing, BentoML, and RayServe will also be discussed, making sure that you have a good understanding of the most important methods and techniques in model serving. Later, you'll cover topics such as monitoring and performance optimization, as well as strategies for managing model drift and handling updates and versioning. The book will provide practical guidance and best practices for ensuring that your model serving pipeline is robust, scalable, and reliable. Additionally, this book will explore the use of cloud-based platforms and services for model serving using AWS SageMaker with the help of detailed examples. By the end of this book, you'll be able to save and serve your model using state-of-the-art techniques. What you will learnExplore specific patterns in model serving that are crucial for every data science professionalUnderstand how to serve machine learning models using different techniquesDiscover the various approaches to stateless servingImplement advanced techniques for batch and streaming model servingGet to grips with the fundamental concepts in continued model evaluationServe machine learning models using a fully managed AWS Sagemaker cloud solutionWho this book is for This book is for machine learning engineers and data scientists who want to bring their models into production. Those who are familiar with machine learning and have experience of using machine learning techniques but are looking for options and strategies to bring their models to production will find great value in this book. Working knowledge of Python programming is a must to get started.

Vols. for 1902- include decisions of the District of Columbia Court of Appeals and various other courts of the District of Columbia.