Cantitate/Preț
Produs
Total produse
Vezi coșul
Reinforcement Learning for Finance de Samit Ahlawat

Reinforcement Learning for Finance

Autor Samit Ahlawat
en Limba Engleză Paperback – 27 dec 2022
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.

Citește tot Restrânge

Preț: 21641 lei

Puncte Express: 325

Carte disponibilă

Livrare economică 21 august-04 septembrie

Livrare prin curier în România Termenul estimat este afișat lângă disponibilitate.
Transport gratuit de la 40000 lei Plată online sau ramburs, în funcție de opțiunile comenzii.
Retur gratuit în 14 zile Comandă securizată și suport în română.
 Adaugă în coș

Wish list
Gift list
Am citit!
Adaugă în listă

Specificații

ISBN-13: 9781484288344
ISBN-10: 1484288343
Pagini: 440
Ilustrații: XV, 423 p. 85 illus., 84 illus. in color.
Dimensiuni: 155 x 235 x 24 mm
Greutate: 0.66 kg
Ediția:1st edition
Editura: Apress
Locul publicării:Berkeley, CA, United States

Cuprins

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.

Notă biografică

Samit Ahlawat is a Senior Vice President in Quantitative Research, Capital Modeling at J.P. Morgan Chase in New York, US. In his current role, he is responsible for building trading strategies for asset management and for building risk management models. His research interests include artificial intelligence, risk management and algorithmic trading strategies. He has given CQF institute talks on artificial intelligence, has authored several research papers in finance and holds a patent for facial recognition technology. In his spare time, he contributes to open source code.

Textul de pe ultima copertă

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.

Caracteristici

Covers reinforcement learning concepts with mathematical theory and practical application Explains cutting-edge advances in reinforcement learning algorithms. Covers convolutional neural networks and recurrent neural networks in reinforcement learning