David Sweet – författare

This textbook helps technical rescue professionals remain safe and capable by delivering the most current practical skills and information available on today's increasingly technical vehicles. To help achieve to achieve the competencies at the awareness, operations, and technical levels the Revised Second Edition of Vehicle Rescue and Extrication: Principles and Practice meets and exceeds all the job performance requirements as outlined in 2021 Edition of NFPA 1006: Standard for Technical Rescue Personnel Professional Qualifications. Whether small, large, or heavy commercial vehicle incident, this resource provides all the critical knowledge and skills necessary for technical rescue personnel to perform successfully in even the most challenging environments.- Skill Drills not only provide step-by-step visual explanation of important skills and procedures, but they do so using powered hydraulic rescue tools and non-powered rescue tools to cater to those with and without access to hydraulic rescue tools.- Get prescriptive guidance at any scene by incorporating the 3-part philosophy reinforced throughout the text: Stabilize the Scene; Stabilize the Vehicle and Stabilize the Patient.- Stay up-to-date with advances in alternative-fuel vehicles, propulsion technology, and other prominent developments in vehicle technology, which are all examined to expand the rescuer's knowledge base.- Prepare to deal with any just about any vehicle. Dedicated chapters on commercial vehicles and school buses are even offered at the Technician level.- Part of a complete teaching and learning system designed to make course delivery simple for educators and provide students with access to helpful tools that cater to a variety of learning styles and needs.Can’t find the digital product you are looking for? Please contact us directly at jbl@mare-nostrum.co.uk to purchase eBooks and Instructor Resources. We’d be happy to help.

Optimise the performance of your systems with practical experiments used by engineers in the world's most competitive industries.

Experimentation for Engineers: From A/B testing to Bayesian optimization is a toolbox of techniques for evaluating new features and fine-tuning parameters. You will 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.

You will learn how to:

Design, run, and analyse 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 optimisation Clearly define business metrics used for decision-making Identify and avoid the common pitfalls of experimentation

By the time you're done, you will be able to seamlessly deploy experiments in production, whilst 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 and will help you enhance machine learning systems, software applications, and quantitative trading solutions.

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