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خرید و دانلود نسخه کامل کتاب Foundations of Reinforcement Learning with Applications in Finance (Chapman & Hall/CRC Mathematics and Artificial Intelligence Series)

قیمت اصلی 2,849,000 تومان بود.قیمت فعلی 2,499,000 تومان است.

تعداد فروش: 42

نویسندگان: Ashwin Rao, Tikhon Jelvis

فرمت: Original PDF صفحات: 522 pages ناشر: Chapman and Hall/CRC تاریخ انتشار نسخه الکترونیکی : December 16, 2022

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شابک: 9781000801057 لینک: https://www.amazon.com/dp/B0BS1C266D


زمان تحویل: حداکثر 24 ساعت

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Foundations of Reinforcement Learning with Applications in Finance aims to demystify Reinforcement Learning, and to make it a practically useful tool for those studying and working in applied areas — especially finance.Reinforcement Learning is emerging as a powerful technique for solving a variety of complex problems across industries that involve Sequential Optimal Decisioning under Uncertainty. Its penetration in high-profile problems like self-driving cars, robotics, and strategy games points to a future where Reinforcement Learning algorithms will have decisioning abilities far superior to humans. But when it comes getting educated in this area, there seems to be a reluctance to jump right in, because Reinforcement Learning appears to have acquired a reputation for being mysterious and technically challenging.This book strives to impart a lucid and insightful understanding of the topic by emphasizing the foundational mathematics and implementing models and algorithms in well-designed Python code, along with robust coverage of several financial trading problems that can be solved with Reinforcement Learning. This book has been created after years of iterative experimentation on the pedagogy of these topics while being taught to university students as well as industry practitioners.FeaturesFocus on the foundational theory underpinning Reinforcement Learning and software design of the corresponding models and algorithmsSuitable as a primary text for courses in Reinforcement Learning, but also as supplementary reading for applied/financial mathematics, programming, and other related coursesSuitable for a professional audience of quantitative analysts or data scientistsBlends theory/mathematics, programming/algorithms and real-world financial nuances while always striving to maintain simplicity and to build intuitive understandingTo access the code base for this book, please go to: https://github.com/TikhonJelvis/RL-book

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