Feedback Loops

Online markets such as Amazon have completely changed retailing, and even small companies can use these e-commerce platforms to compete with the biggest and best in the world. But success requires new ways of thinking, outstanding customer service, and unprecedented speed. Bernie Thompson's small electronics company became one of the first private-label brands to successfully capitalize on Amazon fulfillment, and even Amazon has touted him as a success. Now he shares how you can find similar success. Going well beyond the basics, which are widely available elsewhere, this book is for more sophisticated private-label sellers and manufacturers who recognize that there are no tricks or schemes, only winning strategies, best practices, and tools for overcoming the inertia of discoverability to get the sales flywheel moving. Specific tips and advice include where to focus your energy, how to make data actionable, and how to close the feedback loops that drive successful e-commerce.

An introduction to the mathematical, computational, and analytical techniques used for modeling biological rhythms, presenting tools from many disciplines and example applications. All areas of biology and medicine contain rhythms, and these behaviors are best understood through mathematical tools and techniques. This book offers a survey of mathematical, computational, and analytical techniques used for modeling biological rhythms, gathering these methods for the first time in one volume. Drawing on material from such disciplines as mathematical biology, nonlinear dynamics, physics, statistics, and engineering, it presents practical advice and techniques for studying biological rhythms, with a common language. The chapters proceed with increasing mathematical abstraction. Part I, on models, highlights the implicit assumptions and common pitfalls of modeling, and is accessible to readers with basic knowledge of differential equations and linear algebra. Part II, on behaviors, focuses on simpler models, describing common properties of biological rhythms that range from the firing properties of squid giant axon to human circadian rhythms. Part III, on mathematical techniques, guides readers who have specific models or goals in mind. Sections on “frontiers” present the latest research; “theory” sections present interesting mathematical results using more accessible approaches than can be found elsewhere. Each chapter offers exercises. Commented MATLAB code is provided to help readers get practical experience. The book, by an expert in the field, can be used as a textbook for undergraduate courses in mathematical biology or graduate courses in modeling biological rhythms and as a reference for researchers.

In a world of information technologies, genetic engineering, controversies about established science, and the mysteries of quantum physics, it is at once seemingly impossible and absolutely vital to find ways to make sense of how science, technology, and society connect. In Feedback Loops: Pragmatism about Science & Technology, editors Andrew Wells Garnar and Ashley Shew bring together original writing from philosophers and science and technology studies scholars to provide novel ways of rethinking the relationships among science, technology, education, and society. Through critiquing and exploring the work of philosopher of science and technology Joseph C. Pitt, the authors featured in this volume investigate the complexities of contemporary technoscience, writing on topics ranging from super-computing to pedagogy, engineering to biotechnology patents, and scientific instruments to disability studies. Taken together, these chapters develop an argument about the necessity of using pragmatism to foster a more productive relationship among science, technology and society.

Companies are spending billions on machine learning projects, but it’s money wasted if the models can’t be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You’ll learn the techniques and tools that will cut deployment time from days to minutes, so that you can focus on developing new models rather than maintaining legacy systems. Data scientists, machine learning engineers, and DevOps engineers will discover how to go beyond model development to successfully productize their data science projects, while managers will better understand the role they play in helping to accelerate these projects. Understand the steps to build a machine learning pipeline Build your pipeline using components from TensorFlow Extended Orchestrate your machine learning pipeline with Apache Beam, Apache Airflow, and Kubeflow Pipelines Work with data using TensorFlow Data Validation and TensorFlow Transform Analyze a model in detail using TensorFlow Model Analysis Examine fairness and bias in your model performance Deploy models with TensorFlow Serving or TensorFlow Lite for mobile devices Learn privacy-preserving machine learning techniques

Clearly explaining the logical analysis of biological control phenomena, Biological Feedback answers questions concerning everything from regulation to logic. This rare monograph presents a formal methodology for analyzing the dynamic behavior of complex systems. The easy-to-read text describes a simple logical formalization called "kinetic logic". The reader discovers how this method is used to predict all possible patterns of behavior of which a system is capable. It includes specific conditions required for each pattern. It also explains how to modify an incorrect model in order to account for the observed behavior. The authors give special attention to the two basic types of simple feedback loops: positive and negative. This volume is filled with easy-to-use tables, providing quick reference throughout the book. The subject matter is of great interest to everyone working in molecular genetics and developmental biology. Researchers, immunologists, physical chemists, physicists, electrical engineers, economists, and mathematicians will find this unique text to be an informative, indispensable resource.

The carefully reviewed papers in this state-of-the-art survey describe a wide range of approaches coming from different strands of software engineering, and look forward to future challenges facing this ever-resurgent and exacting field of research.