Reinforcement Learning for Finance : Solve Problems in Finance with CNN and RNN Using the TensorFlow Library / by Samit Ahlawat.

Format:

Book

Author/Creator:

Ahlawat, Samit, author.

Language:

English

Subjects (All):

Machine learning.

Python (Computer program language).

Artificial intelligence.

Machine Learning.

Python.

Artificial Intelligence.

Local Subjects:

Machine Learning.

Python.

Artificial Intelligence.

Physical Description:

1 online resource (435 pages)

Edition:

1st ed. 2023.

Place of Publication:

Berkeley, CA : Apress : Imprint: Apress, 2023.

Summary:

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

Contents:

Chapter 1 Overview

Chapter 2 Introduction to TensorFlow

Chapter 3 Convolutional Neural Networks

Chapter 4 Recurrent Neural Networks

Chapter 5 Reinforcement Learning - Theory

Chapter 6 Recent RL Algorithms.

Notes:

Description based upon print version of record.

Includes bibliographical references and index.

Other Format:

Print version: Ahlawat, Samit Reinforcement Learning for Finance

ISBN:

9781484288351

1484288351

OCLC:

1356574193