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Reinforcement Learning for Finance

Name: Reinforcement Learning for Finance
Brand: Springer Nature B.V.
SKU: 43407544
Price: 44.99 EUR
Availability: InStock
Author: Ahlawat Samit Ahlawat
ISBN: 9781484288368

Solve Problems in Finance with CNN and RNN Using the TensorFlow Library

Ahlawat Samit Ahlawat

Language

English

Book Paperback

Libristo code: 43407544

Publishers Springer Nature B.V., December 2022

This book introduces reinforcement learning with mathematical theory and practical examples from qua... Full description

Libristo code: 43407544

110 b

44.99 € VAT included

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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, andloss 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 For

Data Scientists, Machine Learning engineers and Python programmers who want to apply reinforcement learning to solve problems.