Full color publication. This document has been produced and updated over a 21-year period. It is intended to be a handy reference document, basically one page per flight, and care has been exercised to make it as error-free as possible. This document is basically "as flown" data and has been compiled from many sources including flight logs, flight rules, flight anomaly logs, mod flight descent summary, post flight analysis of mps propellants, FDRD, FRD, SODB, and the MER shuttle flight data and inflight anomaly list. Orbit distance traveled is taken from the PAO mission statistics.
For courses in decision support systems, computerized decision-making tools, and management support systems. Market-leading guide to modern analytics, for better business decisionsAnalytics, Data Science, & Artificial Intelligence: Systems for Decision Support is the most comprehensive introduction to technologies collectively called analytics (or business analytics) and the fundamental methods, techniques, and software used to design and develop these systems. Students gain inspiration from examples of organisations that have employed analytics to make decisions, while leveraging the resources of a companion website. With six new chapters, the 11th edition marks a major reorganisation reflecting a new focus -- analytics and its enabling technologies, including AI, machine-learning, robotics, chatbots, and IoT.
The leading experts in system change and learning, with their school-based partners around the world, have created this essential companion to their runaway best-seller, Deep Learning: Engage the World Change the World. This hands-on guide provides a roadmap for building capacity in teachers, schools, districts, and systems to design deep learning, measure progress, and assess conditions needed to activate and sustain innovation. Dive Into Deep Learning: Tools for Engagement is rich with resources educators need to construct and drive meaningful deep learning experiences in order to develop the kind of mindset and know-how that is crucial to becoming a problem-solving change agent in our global society. Designed in full color, this easy-to-use guide is loaded with tools, tips, protocols, and real-world examples. It includes: • A framework for deep learning that provides a pathway to develop the six global competencies needed to flourish in a complex world — character, citizenship, collaboration, communication, creativity, and critical thinking. • Learning progressions to help educators analyze student work and measure progress. • Learning design rubrics, templates and examples for incorporating the four elements of learning design: learning partnerships, pedagogical practices, learning environments, and leveraging digital. • Conditions rubrics, teacher self-assessment tools, and planning guides to help educators build, mobilize, and sustain deep learning in schools and districts. Learn about, improve, and expand your world of learning. Put the joy back into learning for students and adults alike. Dive into deep learning to create learning experiences that give purpose, unleash student potential, and transform not only learning, but life itself.
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
The Microsoft Technology Associate certification (MTA) curriculum helps instructors teach and validate fundamental technology concepts with a foundation for students' careers as well as the confidence they need to succeed in advanced studies. Through the use of MOAC MTA titles you can help ensure your students future success in and out of the classroom. This MTA text covers the following Windows Operating System vital fundamental skills: • Understanding Operating System Configurations • Installing and Upgrading Client Systems • Managing Applications • Managing Files and Folders • Managing Devices • Understanding Operating System Maintenance. Click here to learn more about Microsoft Technology Associate, (MTA) a new and innovative certification track designed to provide a pathway for future success in technology courses and careers.
The dream of automatic language translation is now closer thanks to recent advances in the techniques that underpin statistical machine translation. This class-tested textbook from an active researcher in the field, provides a clear and careful introduction to the latest methods and explains how to build machine translation systems for any two languages. It introduces the subject's building blocks from linguistics and probability, then covers the major models for machine translation: word-based, phrase-based, and tree-based, as well as machine translation evaluation, language modeling, discriminative training and advanced methods to integrate linguistic annotation. The book also reports the latest research, presents the major outstanding challenges, and enables novices as well as experienced researchers to make novel contributions to this exciting area. Ideal for students at undergraduate and graduate level, or for anyone interested in the latest developments in machine translation.
In the United States, approximately 14 million people have had cancer and more than 1.6 million new cases are diagnosed each year. However, more than a decade after the Institute of Medicine (IOM) first studied the quality of cancer care, the barriers to achieving excellent care for all cancer patients remain daunting. Care often is not patient-centered, many patients do not receive palliative care to manage their symptoms and side effects from treatment, and decisions about care often are not based on the latest scientific evidence. The cost of cancer care also is rising faster than many sectors of medicine--having increased to $125 billion in 2010 from $72 billion in 2004--and is projected to reach $173 billion by 2020. Rising costs are making cancer care less affordable for patients and their families and are creating disparities in patients' access to high-quality cancer care. There also are growing shortages of health professionals skilled in providing cancer care, and the number of adults age 65 and older--the group most susceptible to cancer--is expected to double by 2030, contributing to a 45 percent increase in the number of people developing cancer. The current care delivery system is poorly prepared to address the care needs of this population, which are complex due to altered physiology, functional and cognitive impairment, multiple coexisting diseases, increased side effects from treatment, and greater need for social support. Delivering High-Quality Cancer Care: Charting a New Course for a System in Crisis presents a conceptual framework for improving the quality of cancer care. This study proposes improvements to six interconnected components of care: (1) engaged patients; (2) an adequately staffed, trained, and coordinated workforce; (3) evidence-based care; (4) learning health care information technology (IT); (5) translation of evidence into clinical practice, quality measurement and performance improvement; and (6) accessible and affordable care. This report recommends changes across the board in these areas to improve the quality of care. Delivering High-Quality Cancer Care: Charting a New Course for a System in Crisis provides information for cancer care teams, patients and their families, researchers, quality metrics developers, and payers, as well as HHS, other federal agencies, and industry to reevaluate their current roles and responsibilities in cancer care and work together to develop a higher quality care delivery system. By working toward this shared goal, the cancer care community can improve the quality of life and outcomes for people facing a cancer diagnosis.
