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"Nad Niemnem, the Polish original of this work, was first published in book form in 1888"--Translator's notes.
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Role-play as a Heritage Practice is the first book to examine physically performed role-enactments, such as live-action role-play (LARP), tabletop role-playing games (TRPG), and hobbyist historical reenactment (RH), from a combined game studies and heritage studies perspective. Demonstrating that non-digital role-plays, such as TRPG and LARP, share many features with RH, the book contends that all three may be considered as heritage practices. Studying these role-plays as three distinct genres of playful, participatory and performative forms of engagement with cultural heritage, Mochocki demonstrates how an exploration of the affordances of each genre can be valuable. Showing that a playerâ€...
Comparative case studies of how memories of World War II have been constructed and revised in France, Germany, Austria, Switzerland, Poland, Italy, and the USSR (Russia).
Solve your AI and machine learning problems using complete and real-world code examples. Using a problem-solution approach, this book makes deep learning and machine learning accessible to everyday developers, by providing a combination of tools such as cognitive services APIs, machine learning platforms, and libraries. Along with an overview of the contemporary technology landscape, Machine Learning and Deep Learning with Cognitive Computing Recipes covers the business case for machine learning and deep learning. Covering topics such as digital assistants, computer vision, text analytics, speech, and robotics process automation this book offers a comprehensive toolkit that you can apply qui...
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.