Reinforcement Learning for Finance – Apress (2023)
English | eBook | Size: 17.62 MB
Explore the innovative intersection of reinforcement learning and finance through this comprehensive guide. With a foundation in mathematical theory and hands-on examples using TensorFlow, this book offers a deep dive into reinforcement learning’s practical applications in quantitative finance. Starting with an overview of TensorFlow for neural network training, it delves into Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), crucial for deep learning in reinforcement learning tasks. The book meticulously covers reinforcement learning theory, including Markov decision processes and recent algorithms, through the lens of solving complex financial problems. Whether you’re a data scientist, machine learning engineer, or Python programmer, this book equips you with the knowledge to apply reinforcement learning techniques to the financial sector, enhancing your understanding of market dynamics and risk management strategies.
What You’ll Learn
Master the fundamentals of reinforcement learning and its application in finance.
Implement CNNs and RNNs for deep learning tasks in reinforcement learning scenarios.
Navigate the intricacies of Markov decision processes and policy gradients.
Leverage recent reinforcement learning algorithms using TensorFlow to solve finance-related problems.
Who This Book Is For
Data scientists, machine learning engineers, and Python programmers interested in applying reinforcement learning within the financial sector. This guide serves as a bridge between the theoretical aspects of reinforcement learning and its practical applications, providing the tools necessary to tackle challenges in finance with confidence.
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