Reinforcement Learning for Finance

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος:	Reinforcement Learning for Finance
Περιγραφή:	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 LearnUnderstand the fundamentals of reinforcement learningApply reinforcement learning programming techniques to solve quantitative-finance problemsGain insight into convolutional neural networks and recurrent neural networksUnderstand the Markov decision processWho This Book Is ForData Scientists, Machine Learning engineers and Python programmers who want to apply reinforcement learning to solve problems.
Συγγραφείς:	Samit Ahlawat
Resource Type:	eBook.
Θέματα:	Python (Computer program language), Reinforcement learning, Finance--Mathematical models--Data processing
Categories:	MATHEMATICS / Probability & Statistics / General, COMPUTERS / Artificial Intelligence / General, COMPUTERS / Languages / Python
Βάση Δεδομένων:	eBook Index

FullText	Text: Availability: 0
Header	DbId: edsebk DbLabel: eBook Index An: 3511528 RelevancyScore: 962 AccessLevel: 6 PubType: eBook PubTypeId: ebook PreciseRelevancyScore: 962.24267578125
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Items	– Name: Title Label: Title Group: Ti Data: Reinforcement Learning for Finance – Name: Abstract Label: Description Group: Ab Data: 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 LearnUnderstand the fundamentals of reinforcement learningApply reinforcement learning programming techniques to solve quantitative-finance problemsGain insight into convolutional neural networks and recurrent neural networksUnderstand the Markov decision processWho This Book Is ForData Scientists, Machine Learning engineers and Python programmers who want to apply reinforcement learning to solve problems. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Samit+Ahlawat%22">Samit Ahlawat</searchLink> – Name: TypePub Label: Resource Type Group: TypPub Data: eBook. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Python+%28Computer+program+language%29%22">Python (Computer program language)</searchLink><br /><searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink><br /><searchLink fieldCode="DE" term="%22Finance--Mathematical+models--Data+processing%22">Finance--Mathematical models--Data processing</searchLink> – Name: SubjectBISAC Label: Categories Group: Su Data: <searchLink fieldCode="ZK" term="%22MATHEMATICS+%2F+Probability+%26+Statistics+%2F+General%22">MATHEMATICS / Probability & Statistics / General</searchLink><br /><searchLink fieldCode="ZK" term="%22COMPUTERS+%2F+Artificial+Intelligence+%2F+General%22">COMPUTERS / Artificial Intelligence / General</searchLink><br /><searchLink fieldCode="ZK" term="%22COMPUTERS+%2F+Languages+%2F+Python%22">COMPUTERS / Languages / Python</searchLink>
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RecordInfo	BibRecord: BibEntity: Classifications: – Code: 332.0285631 Scheme: ddc Type: prePub Languages: – Code: eng Text: English Subjects: – SubjectFull: Python (Computer program language) Type: general – SubjectFull: Reinforcement learning Type: general – SubjectFull: Finance--Mathematical models--Data processing Type: general Titles: – TitleFull: Reinforcement Learning for Finance Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Samit Ahlawat – PersonEntity: Name: NameFull: Samit Ahlawat IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2022 – D: 06 M: 01 Type: profile Y: 2023 Identifiers: – Type: isbn-print Value: 9781484288344 – Type: isbn-electronic Value: 9781484288351 Titles: – TitleFull: Reinforcement Learning for Finance Type: main
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