Carte Reinforcement Learning for Finance Samit Ahlawat

Reinforcement Learning for Finance

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

Autor: Samit Ahlawat
Limbă: engleză
Legare: Carte broșată
Editura: APress
Disponibilitate: În depozitul extern
Expediem în 14-21 zile
156.63 lei
This book introduces reinforcement learning with mathematical theory and practical examples from qua...

Informații despre carte

Limbă
engleză
Legare
Carte - Carte broșată
Publicat
2022
Pagini
134
EAN
9781484288344
Enbook ID
41424736
Editura
Greutate
663
Dimensiuni
155 x 235 x 24

Descriere completă

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 gradient 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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