Peter Krause – författare
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Discover how desktop flows can interact with your everyday tools and automate tasks, freeing up time to do more important thingsPurchase of the print or Kindle book includes a free PDF eBook
Key Features
Learn how Office programs can assist with automating recurring tasksMaintain superior work quality by including daily desktop and web applications in your flowsEnrich your flows with additional AI-based information and integrate them with cloud systemsBook Description
Whether you want to organize simple files or perform more complex consolidations between different Office programs and remote-control applications that don''t allow outside access, Power Automate Desktop helps meet these challenges. This book shows you how to leverage this workflow automation platform by explaining the underlying RPA concepts in a step-by-step way.You’ll start with simple flows that can be easily recorded and further processed using the built-in recorder. Later, you’ll learn how to use the more advanced actions to automate folder and file management and enable Office programs to interact with each other. You’ll also get to grips with integrating desktop flows into other cloud environments and further enhance their value using AI. As you progress, you’ll understand how flows can run unattended and how they are managed in the Power Platform, as well as key concepts such as creating, modifying, debugging, and error-handling UI flows. Finally, the book will guide you to use Process Automation Designer (PAD) in conjunction with your frequently used desktop systems to automate routine tasks.By the end of this book, you’ll have become a Power Automate Desktop expert, automating both professional and personal tasks.What you will learn
Master RPA with Power Automate Desktop to commence your debut flowGrasp all essential product concepts such as UI flow creation and modification, debugging, and error handlingUse PAD to automate tasks in conjunction with the frequently used systems on your desktopAttain proficiency in configuring flows that run unattended to achieve seamless automationDiscover how to use AI to enrich your flows with insights from different AI modelsExplore how to integrate a flow in a broader cloud contextWho this book is for
Whether you’re a home user looking to automate simple tasks on your workstation or a business user or citizen developer seeking to automate more complex rule-based processes, this book will help you overcome the challenge. No knowledge of a programming language is required, but in the more advanced chapters, a general understanding of information technology, including basic programming language structures, protocols, and cloud concepts, will be helpful.
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Evolution strategies have more than 50 years of history in the field of evolutionary computation. Since the early 1990s, many algorithmic variations of evolution strategies have been developed, characterized by the fact that they use the so-called derandomization concept for strategy parameter adaptation. Most importantly, the covariance matrix adaptation strategy (CMA-ES) and its successors are the key representatives of this group of contemporary evolution strategies.
This book provides an overview of the key algorithm developments between 1990 and 2012, including brief descriptions of the algorithms, a unified pseudocode representation of each algorithm, and program code which is available for download. In addition, a taxonomy of these algorithms is provided to clarify similarities and differences as well as historical relationships between the various instances of evolution strategies. Moreover, due to the authors’ focus on industrial applications of nonlinear optimization, all algorithms are empirically compared on the so-called BBOB (Black-Box Optimization Benchmarking) test function suite, and ranked according to their performance. In contrast to classical academic comparisons, however, only a very small number of objective function evaluations is permitted. In particular, an extremely small number of evaluations, such as between one hundred and one thousand for high-dimensional functions, is considered. This is motivated by the fact that many industrial optimization tasks do not permit more than a few hundred evaluations. Our experiments suggest that evolution strategies are powerful nonlinear direct optimizers even for challenging industrial problems with a very small budget of function evaluations.
The book is suitable for academic and industrial researchers and practitioners.
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