Presenting a number of company's pilots and real-world projects applying popular and "rare" algorithms of machine learning, as well as technical implementation on different platforms such as Java, PHP, Python using open libraries and a range of Amazon Web Services tools.
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Will be later
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Discussing generative and discriminative models' connections in terms of program specialization, their role within the framework of deep learning and probabilistic programming. Addressing neural Bayesian approach, neural probabilistic programming as an integration of two paradigms on an example of Edward library.
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This talk is about a distributed web-crawler for search and extraction of contact information from corporate websites.
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Access to government data opens new opportunities for the businesses ready to create new products based on that data and improve existing ones, but it requires understanding of how data is collected, analyzed and published.
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The talk covers different data processing and storage technologies from Hadoop ecosystem and more. If you are doing ML not just for fun, but also for profit, you might get some useful information from the talk.
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