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    How Much Does it Cost for a New Leach Field? Also known as a drain field or soil absorption system, leach fields are an integral part of a septic system. If the leach field fails, replacing it can be costly.

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    A soft water is aggressive at leaching metals (like lead) from your lines and faucets. Most faucets are solid brass (with a relatively high lead content) and are chrome plated. This means that if you have soft water, there is a great chance that your initial drawing of cold water will have a .

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    About the Program. Duke has a vibrant center for research in machine learning. We are active in most major areas of ML and in a variety of applications like vision, computational biology, the Web, social networks, neuroscience, healthcare, robotics, causal outcomes, and communication systems.

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    A Boltzmann machine (also called stochastic Hopfield network with hidden units) is a type of stochastic recurrent neural network (and Markov random field [citation needed]). Boltzmann machines can be seen as the stochastic, generative counterpart of Hopfield networks.

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    Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, you''ll learn techniques for extracting and transforming features—the numeric representations of raw data—into formats for machine-learning models.

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    The practical application of artificial intelligence and machine learning will become a standard way of life across engineering, testing, and broader portfolio management in the near future.

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    Amnesia: A Machine for Pigs is a survival horror video game developed by The Chinese Room and published by Frictional Games. Originally meant to be a mod by The Chinese Room, the game is an indirect sequel to Amnesia: The Dark Descent, which was both developed and produced by .

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    The Center for Trustworthy Machine Learning (CTML) is an Frontier in Secure & Trustworthy Computing, and it is supported by the National Science Foundation.. The focus of the Center is to develop a rigorous understanding of the vulnerabilities inherent to machine learning, and to develop the tools, metrics, and methods to mitigate them.

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