Building Intelligent Systems A Guide To Machine Learning Engineering By Geoff Hulten
Download Building Intelligent Systems A Guide To Machine Learning Engineering By Geoff Hulten PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Building Intelligent Systems A Guide To Machine Learning Engineering By Geoff Hulten book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Building Intelligent Systems
Produce a fully functioning Intelligent System that leverages machine learning and data from user interactions to improve over time and achieve success. This book teaches you how to build an Intelligent System from end to end and leverage machine learning in practice. You will understand how to apply your existing skills in software engineering, data science, machine learning, management, and program management to produce working systems. Building Intelligent Systems is based on more than a decade of experience building Internet-scale Intelligent Systems that have hundreds of millions of user interactions per day in some of the largest and most important software systems in the world. What You’ll Learn Understand the concept of an Intelligent System: What it is good for, when you need one, and how to set it up for success Design an intelligent user experience: Produce data to help make the Intelligent System better over time Implement an Intelligent System: Execute, manage, and measure Intelligent Systems in practice Create intelligence: Use different approaches, including machine learning Orchestrate an Intelligent System: Bring the parts together throughout its life cycle and achieve the impact you want Who This Book Is For Software engineers, machine learning practitioners, and technical managers who want to build effective intelligent systems
The Fluxus Reader
Part I. Three histories : Developing a fluxable forum: Early performance & publishing / Owen Smith -- Fluxus, fluxion, flushoe: the 1970's / Simon Anderson -- Fluxus fortuna / Hannah Higgins -- Part II. Theories of Fluxus: Boredom and oblivion / Ina Blon -- Zen vaudeville: a medi(t)ation in the margins of Fluxus / David T. Doris -- Fluxus as a laboratory / Craig Saper -- Part III. Critical and historical perspectives: Fluxus history and trans-history: competing strategies for empowerment / Estera Milman -- Historical design and social purpose: a note on the relationship of Fluxus to modernism / Stephen C. Foster -- A spirit of large goals: fluxus, dada and postmodern cultural theory at two speeds -- Part IV. Three Fluxus voices : Transcript of the videotaped Interview with George Maciunas -- Selections from an interview with Billie Maciunas / Susan L. Jarosi -- Maybe Fluxus (a para-interrogative guide for the neoteric transmuter, tinder, tinker and totalist) / Larry Miller -- Part V. Two Fluxus theories : Fluxus : theory and reception / Dick Higgins -- Fluxus and company / Ken Friedman -- Part. VI-- Documents of Fluxus : Fluxus chronology : key moments and events -- A list of selected Fluxus art works and related primary source materials -- A list of selected Fluxus sources and related secondary sources.
Machine Learning in Production
A practical and innovative textbook detailing how to build real-world software products with machine learning components, not just models. Traditional machine learning texts focus on how to train and evaluate the machine learning model, while MLOps books focus on how to streamline model development and deployment. But neither focus on how to build actual products that deliver value to users. This practical textbook, by contrast, details how to responsibly build products with machine learning components, covering the entire development lifecycle from requirements and design to quality assurance and operations. Machine Learning in Production brings an engineering mindset to the challenge of building systems that are usable, reliable, scalable, and safe within the context of real-world conditions of uncertainty, incomplete information, and resource constraints. Based on the author’s popular class at Carnegie Mellon, this pioneering book integrates foundational knowledge in software engineering and machine learning to provide the holistic view needed to create not only prototype models but production-ready systems. • Integrates coverage of cutting-edge research, existing tools, and real-world applications • Provides students and professionals with an engineering view for production-ready machine learning systems • Proven in the classroom • Offers supplemental resources including slides, videos, exams, and further readings