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Deep Learning in Quantitative Finance by Andrew Green
Deep Learning in Quantitative Finance by Andrew Green
Deep Learning in Quantitative Finance by Andrew Green

Deep Learning in Quantitative Finance by Andrew Green

Deep learning, that is the use of deep neural networks, is now one of the hottest topics amongst quantitative analysts. This book provides a comprehensive treatment of deep learning and a wide range of applications in mainstream quantitative finance. The book introduces the basics of neural networks including feedforward networks, optimization and training and regularization techniques, before proceeding to cover more advanced topics including CNNs, RNNs, autoencoders, generative models and deep reinforcement learning. The main software frameworks, Tensorflow and Pytorch, are introduced and discussed, along with a number of others. The book then proceeds to cover the very latest deep learning research in quantitative finance, including approximating derivative values, high dimensional PDE solvers and BSDEs, volatility models and model calibration, credit curve mapping for XVA, generating realistic market data, order book management and hedging using reinforcement learning. The book concludes with a look at the potential for quantum deep learning and the broader implications deep learning has for quantitative finance and quantitative analysts.

Deep learning, that is the use of deep neural networks, is now one of the hottest topics amongst quantitative analysts. This book provides a comprehensive treatment of deep learning and a wide range of applications in mainstream quantitative finance. The book introduces the basics of neural networks including feedforward networks, optimization and training and regularization techniques, before proceeding to cover more advanced topics including CNNs, RNNs, autoencoders, generative models and deep reinforcement learning. The main software frameworks, Tensorflow and Pytorch, are introduced and discussed, along with a number of others. The book then proceeds to cover the very latest deep learning research in quantitative finance, including approximating derivative values, high dimensional PDE solvers and BSDEs, volatility models and model calibration, credit curve mapping for XVA, generating realistic market data, order book management and hedging using reinforcement learning. The book concludes with a look at the potential for quantum deep learning and the broader implications deep learning has for quantitative finance and quantitative analysts.

$132.99

in 1 offers

Deep Learning in Quantitative Finance by Andrew Green

$132.99

Deep learning, that is the use of deep neural networks, is now one of the hottest topics amongst quantitative analysts. This book provides a comprehensive treatment of deep learning and a wide range of applications in mainstream quantitative finance. The book introduces the basics of neural networks including feedforward networks, optimization and training and regularization techniques, before proceeding to cover more advanced topics including CNNs, RNNs, autoencoders, generative models and deep reinforcement learning. The main software frameworks, Tensorflow and Pytorch, are introduced and discussed, along with a number of others. The book then proceeds to cover the very latest deep learning research in quantitative finance, including approximating derivative values, high dimensional PDE solvers and BSDEs, volatility models and model calibration, credit curve mapping for XVA, generating realistic market data, order book management and hedging using reinforcement learning. The book concludes with a look at the potential for quantum deep learning and the broader implications deep learning has for quantitative finance and quantitative analysts.

Deep learning, that is the use of deep neural networks, is now one of the hottest topics amongst quantitative analysts. This book provides a comprehensive treatment of deep learning and a wide range of applications in mainstream quantitative finance. The book introduces the basics of neural networks including feedforward networks, optimization and training and regularization techniques, before proceeding to cover more advanced topics including CNNs, RNNs, autoencoders, generative models and deep reinforcement learning. The main software frameworks, Tensorflow and Pytorch, are introduced and discussed, along with a number of others. The book then proceeds to cover the very latest deep learning research in quantitative finance, including approximating derivative values, high dimensional PDE solvers and BSDEs, volatility models and model calibration, credit curve mapping for XVA, generating realistic market data, order book management and hedging using reinforcement learning. The book concludes with a look at the potential for quantum deep learning and the broader implications deep learning has for quantitative finance and quantitative analysts.

Price comparison

Last updated at 18/07/2025 15:51:18

QBD Books

$106.21

available 7 days ago

Low stock


cashrewards

3.8% cashback

shopback

3.5% Cashback

MightyApe.com.au

$132.99

+ $15.00 delivery

Go to store

See 3 more history offers

Booktopia.com.au

$172.35

available 25 days ago

Low stock


cashrewards

8% cashback

shopback

8% Cashback

Dymocks

$218.99

available 6 months ago

Low stock


cashrewards

Up to 1.8% cashback

shopback

Up to 2.1% Cashback

The Nile Australia

$93.71

available over 1 year ago

Low stock


cashrewards

1.6% cashback

Price history

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Price history

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Specification

Length244.0 millimetre

Price comparison

Updated about 17 hours ago
QBD Books

$106.21

Low Stock

cashrewards

3.8% cashback


shopback

3.5% Cashback

MightyApe.com.au

$132.99

+$15.00 delivery

See 3 more history offers

Booktopia.com.au

$172.35

Low Stock

cashrewards

8% cashback


shopback

8% Cashback

Dymocks

$218.99

Low Stock

cashrewards

Up to 1.8% cashback


shopback

Up to 2.1% Cashback

The Nile Australia

$93.71

Low Stock

cashrewards

1.6% cashback

Price history

​
​

Price history

​
​

Specification

Length244.0 millimetre