Reinforcement learning for finance (Record no. 233747)

MARC details
000 -LEADER
fixed length control field 01621nam a22002057a 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20250211111741.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 250211b ||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781484294055
040 ## - CATALOGING SOURCE
Transcribing agency AL
041 ## - LANGUAGE CODE
Language code of text/sound track or separate title eng
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Edition number 23
Classification number 332
Item number AHLR
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Ahlawat Samit
9 (RLIN) 198943
245 ## - TITLE STATEMENT
Title Reinforcement learning for finance
Remainder of title : solve problems in finance with CNN and RNN using the tensorflow library
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. New York
Name of publisher, distributor, etc. Apress
Date of publication, distribution, etc. 2023
300 ## - PHYSICAL DESCRIPTION
Extent xv,423p
Other physical details PB
Dimensions 23x15cm.
365 ## - TRADE PRICE
Source of price type code Genral
Price type code 6391
Price amount ₹959.20
Currency code ₹
Unit of pricing ₹1199.00
Price note 20%
Price effective from 6/02/2025
520 ## - SUMMARY, ETC.
Summary, etc. 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.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Source of heading or term Economics
Topical term or geographic name entry element Financial Economics
9 (RLIN) 198944
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Koha item type Book
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Home library Current library Date acquired Source of acquisition Cost, normal purchase price Total Checkouts Full call number Barcode Date last seen Cost, replacement price Price effective from Koha item type
    Dewey Decimal Classification     M Com St Aloysius PG Library St Aloysius PG Library 02/08/2025 Biblios Book Point 959.20   332 AHLR PG024845 02/11/2025 1199.00 02/11/2025 Book