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Reinforcement Learning for Finance - Solve Problems in Finance with CNN and RNN Using the TensorFlow Library (Paperback, 1st ed.)
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Reinforcement Learning for Finance - Solve Problems in Finance with CNN and RNN Using the TensorFlow Library (Paperback, 1st ed.)
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This book introduces reinforcement learning with mathematical
theory and practical examples from quantitative finance using the
TensorFlow library. Reinforcement Learning for Finance begins by
describing methods for training neural networks. Next, it discusses
CNN and RNN - two kinds of neural networks used as deep learning
networks in reinforcement learning. Further, the book dives into
reinforcement learning theory, explaining the Markov decision
process, value function, policy, and policy gradients, with their
mathematical formulations and learning algorithms. It covers recent
reinforcement learning algorithms from double deep-Q networks to
twin-delayed deep deterministic policy gradients and generative
adversarial networks with examples using the TensorFlow Python
library. It also serves as a quick hands-on guide to TensorFlow
programming, covering concepts ranging from variables and graphs to
automatic differentiation, layers, models, and loss functions.
After completing this book, you will understand reinforcement
learning with deep q and generative adversarial networks using the
TensorFlow library. What You Will Learn Understand the fundamentals
of reinforcement learning Apply reinforcement learning programming
techniques to solve quantitative-finance problems Gain insight into
convolutional neural networks and recurrent neural networks
Understand the Markov decision process Who This Book Is ForData
Scientists, Machine Learning engineers and Python programmers who
want to apply reinforcement learning to solve problems.
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