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Reinforcement Learning and Interpretability
Manage episode 320264314 series 2953248
内容由The Quant / Financial Engineering Podcast and Patrick J Zoro提供。所有播客内容（包括剧集、图形和播客描述）均由 The Quant / Financial Engineering Podcast and Patrick J Zoro 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品，您可以按照此处概述的流程进行操作https://zh.player.fm/legal。
Patrick Zoro welcomes to his podcasts Hariom Tatsat author of the book "Machine Learning and Data Science Blueprints for Finance: From Building Trading Strategies to Robo-Advisors Using Python 1st Edition", Bryan Yekelchik Lehigh MFE graduate and Zach Coriarty 4th Year, Bachelors of Science in Computer Science and Business at Lehigh University, Interested in data science and ML, LinkedIn: https://www.linkedin.com/in/zachary-coriarty/ They discuss their recent paper on "Deep Q-Network Interpertability: Applications to ETF Trading" https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3973146 https://www.svedbergopen.com/files/1643786733_(3)_IJAIML2021YH205248CR_(p_61-70).pdf
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