Experiment!
Title | Experiment! PDF eBook |
Author | Colin McFarland |
Publisher | New Riders |
Pages | 219 |
Release | 2012-08-17 |
Genre | Computers |
ISBN | 0133040089 |
Testing is a surefire way to dramatically improve your website’s conversion rate and increase revenue. When you run experiments with changes to design or content, you’ll quickly discover which changes better motivate your users to take action. This book shows how to learn from your customers’ behavior and decisions, and how their responses reveal the strengths and weaknesses of your site. It will show you how to make websites that work harder and convert better. Experiment! will inspire you to challenge assumptions and start experimenting right now. You will: Learn how to approach experiments to improve conversion Understand the various methods of testing including A/B and multivariate Discover experiment ideas, and go beyond optimization to innovation Recognize the UX and design implications of experimenting Learn to analyze data and deliver results Experimenting changes the way you think about design and the way you work. It helps prevent the loudest voice from deciding direction; instead, through an experiment, you’ll ask the most important voices--your customers--“What do you think?”
The American Experiment
Title | The American Experiment PDF eBook |
Author | David M. Rubenstein |
Publisher | Simon and Schuster |
Pages | 464 |
Release | 2021-09-07 |
Genre | History |
ISBN | 1982165804 |
THE INSTANT NEW YORK TIMES AND WALL STREET JOURNAL BESTSELLER The capstone book in a trilogy from the New York Times bestselling author of How to Lead and The American Story and host of Bloomberg TV’s The David Rubenstein Show—American icons and historians on the ever-evolving American experiment, featuring Ken Burns, Madeleine Albright, Wynton Marsalis, Billie Jean King, Henry Louis Gates Jr., and many more. In this lively collection of conversations—the third in a series from David Rubenstein—some of our nations’ greatest minds explore the inspiring story of America as a grand experiment in democracy, culture, innovation, and ideas. -Jill Lepore on the promise of America -Madeleine Albright on the American immigrant -Ken Burns on war -Henry Louis Gates Jr. on reconstruction -Elaine Weiss on suffrage -John Meacham on civil rights -Walter Isaacson on innovation -David McCullough on the Wright Brothers -John Barry on pandemics and public health -Wynton Marsalis on music -Billie Jean King on sports -Rita Moreno on film Exploring the diverse make-up of our country’s DNA through interviews with Pulitzer Prize–winning historians, diplomats, music legends, and sports giants, The American Experiment captures the dynamic arc of a young country reinventing itself in real-time. Through these enlightening conversations, the American spirit comes alive, revealing the setbacks, suffering, invention, ingenuity, and social movements that continue to shape our vision of what America is—and what it can be.
Classic Experiments in Psychology
Title | Classic Experiments in Psychology PDF eBook |
Author | Douglas G. Mook |
Publisher | Greenwood |
Pages | 392 |
Release | 2004-12-30 |
Genre | Psychology |
ISBN |
The typical survey course in psychology has time for only limited presentation of the research on which our knowledge is based. As a result, many students come away with a limited understanding of the role of experiments in psychological science. Where do experiments come from and how are they conducted? What are the pitfalls and how can we avoid them? What advantages do they have over intuition, authority, and common sense as guides to knowing and acting? What distinguishes research-based psychology from psychobabble? What have we learned from experimentation in psychology? This book presents, in more depth than textbook treatment permits, the background, conduct, and implications of a selection of classic experiments in psychology. The selection is designed to be diverse, showing that even for research in vastly different areas of study, the logic of research remains the same—as do its traps and pitfalls. This book will broaden and deepen the understanding of experimental methods in psychological research, examining where the research questions come from, how questions can be turned into experiments, and how researchers have faced the problems presented by research in psychology.
Journal
Title | Journal PDF eBook |
Author | South African Institute of Mining and Metallurgy |
Publisher | |
Pages | 494 |
Release | 1909 |
Genre | Mineral industries |
ISBN |
Results of Experiments
Title | Results of Experiments PDF eBook |
Author | Canada. Experimental Sub-Station, Beaverlodge, Alberta |
Publisher | |
Pages | 700 |
Release | 1925 |
Genre | |
ISBN |
Experiment Station Record
Title | Experiment Station Record PDF eBook |
Author | United States. Office of Experiment Stations |
Publisher | |
Pages | 1028 |
Release | 1911 |
Genre | Agricultural experiment stations |
ISBN |
Experimentation for Engineers
Title | Experimentation for Engineers PDF eBook |
Author | David Sweet |
Publisher | Simon and Schuster |
Pages | 246 |
Release | 2023-03-21 |
Genre | Computers |
ISBN | 1638356904 |
Optimize the performance of your systems with practical experiments used by engineers in the world’s most competitive industries. In Experimentation for Engineers: From A/B testing to Bayesian optimization you will learn how to: Design, run, and analyze an A/B test Break the "feedback loops" caused by periodic retraining of ML models Increase experimentation rate with multi-armed bandits Tune multiple parameters experimentally with Bayesian optimization Clearly define business metrics used for decision-making Identify and avoid the common pitfalls of experimentation Experimentation for Engineers: From A/B testing to Bayesian optimization is a toolbox of techniques for evaluating new features and fine-tuning parameters. You’ll start with a deep dive into methods like A/B testing, and then graduate to advanced techniques used to measure performance in industries such as finance and social media. Learn how to evaluate the changes you make to your system and ensure that your testing doesn’t undermine revenue or other business metrics. By the time you’re done, you’ll be able to seamlessly deploy experiments in production while avoiding common pitfalls. About the technology Does my software really work? Did my changes make things better or worse? Should I trade features for performance? Experimentation is the only way to answer questions like these. This unique book reveals sophisticated experimentation practices developed and proven in the world’s most competitive industries that will help you enhance machine learning systems, software applications, and quantitative trading solutions. About the book Experimentation for Engineers: From A/B testing to Bayesian optimization delivers a toolbox of processes for optimizing software systems. You’ll start by learning the limits of A/B testing, and then graduate to advanced experimentation strategies that take advantage of machine learning and probabilistic methods. The skills you’ll master in this practical guide will help you minimize the costs of experimentation and quickly reveal which approaches and features deliver the best business results. What's inside Design, run, and analyze an A/B test Break the “feedback loops” caused by periodic retraining of ML models Increase experimentation rate with multi-armed bandits Tune multiple parameters experimentally with Bayesian optimization About the reader For ML and software engineers looking to extract the most value from their systems. Examples in Python and NumPy. About the author David Sweet has worked as a quantitative trader at GETCO and a machine learning engineer at Instagram. He teaches in the AI and Data Science master's programs at Yeshiva University. Table of Contents 1 Optimizing systems by experiment 2 A/B testing: Evaluating a modification to your system 3 Multi-armed bandits: Maximizing business metrics while experimenting 4 Response surface methodology: Optimizing continuous parameters 5 Contextual bandits: Making targeted decisions 6 Bayesian optimization: Automating experimental optimization 7 Managing business metrics 8 Practical considerations