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The Sentinel
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Army R, D & A.
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The NeurIPS '18 Competition
This volume presents the results of the Neural Information Processing Systems Competition track at the 2018 NeurIPS conference. The competition follows the same format as the 2017 competition track for NIPS. Out of 21 submitted proposals, eight competition proposals were selected, spanning the area of Robotics, Health, Computer Vision, Natural Language Processing, Systems and Physics. Competitions have become an integral part of advancing state-of-the-art in artificial intelligence (AI). They exhibit one important difference to benchmarks: Competitions test a system end-to-end rather than evaluating only a single component; they assess the practicability of an algorithmic solution in addition to assessing feasibility. The eight run competitions aim at advancing the state of the art in deep reinforcement learning, adversarial learning, and auto machine learning, among others, including new applications for intelligent agents in gaming and conversational settings, energy physics, and prosthetics.
‘True Biographies of Nations?’
Dictionaries of national biography are a long-established and significant genre of biographical and historical writing, existing in many forms across the globe. This book brings together practitioners from around the English‑speaking world to reflect on national biographical dictionary projects’ recent cultural journeys, and the challenges presented to them by such developments as the transition to a digital environment, a new alertness to the need to represent diversity, and the rise of transnationalism. Exploring their paths forward, the chapters of this book collectively make a powerful argument for the continued value and importance of large‑scale collaborative biographical dictionary research.
The History Manifesto
A call to arms to historians and everyone interested in history in contemporary society. This title is also available as Open Access.
Technical Debt in Practice
The practical implications of technical debt for the entire software lifecycle; with examples and case studies. Technical debt in software is incurred when developers take shortcuts and make ill-advised technical decisions in the initial phases of a project, only to be confronted with the need for costly and labor-intensive workarounds later. This book offers advice on how to avoid technical debt, how to locate its sources, and how to remove it. It focuses on the practical implications of technical debt for the entire software life cycle, with examples and case studies from companies that range from Boeing to Twitter. Technical debt is normal; it is part of most iterative development process...
Liberal Education: Analog Dreams in a Digital Age
The essays in this collection contemplate the various intersections and barriers between artificial intelligence along with the values and practices of liberal education. For the proponents of liberal education as a core component of undergraduate education, the study of literature, history, philosophy, and the social sciences, like their objects and their forms of practice, are perceived to be about what is essentially human. In spheres previously thought to be exclusively human domains, modern, digitally-constructed artificial intelligence has profound implications for liberal studies, how they may be practiced, and why they are important. This collection explores the implications of AI an...
Building Machine Learning Pipelines
Companies are spending billions on machine learning projects, but it’s money wasted if the models can’t be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You’ll learn the techniques and tools that will cut deployment time from days to minutes, so that you can focus on developing new models rather than maintaining legacy systems. Data scientists, machine learning engineers, and DevOps engineers will discover how to go beyond model development to successfully productize their data science projects, while managers will better understand the role they play ...
MACHINE LEARNING
THE MACHINE LEARNING MCQ (MULTIPLE CHOICE QUESTIONS) SERVES AS A VALUABLE RESOURCE FOR INDIVIDUALS AIMING TO DEEPEN THEIR UNDERSTANDING OF VARIOUS COMPETITIVE EXAMS, CLASS TESTS, QUIZ COMPETITIONS, AND SIMILAR ASSESSMENTS. WITH ITS EXTENSIVE COLLECTION OF MCQS, THIS BOOK EMPOWERS YOU TO ASSESS YOUR GRASP OF THE SUBJECT MATTER AND YOUR PROFICIENCY LEVEL. BY ENGAGING WITH THESE MULTIPLE-CHOICE QUESTIONS, YOU CAN IMPROVE YOUR KNOWLEDGE OF THE SUBJECT, IDENTIFY AREAS FOR IMPROVEMENT, AND LAY A SOLID FOUNDATION. DIVE INTO THE MACHINE LEARNING MCQ TO EXPAND YOUR MACHINE LEARNING KNOWLEDGE AND EXCEL IN QUIZ COMPETITIONS, ACADEMIC STUDIES, OR PROFESSIONAL ENDEAVORS. THE ANSWERS TO THE QUESTIONS ARE PROVIDED AT THE END OF EACH PAGE, MAKING IT EASY FOR PARTICIPANTS TO VERIFY THEIR ANSWERS AND PREPARE EFFECTIVELY.
