Ahlawat samit (44 risultati)

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- Prima edizione
Da: Rarewaves USA, OSWEGO, IL, U.S.A.Rarewaves USA
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EUR 34,81
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Paperback. Condizione: New. 1st ed. 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.

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- Prima edizione
Da: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail
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Paperback. Condizione: new. Paperback. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Da: BargainBookStores, Grand Rapids, MI, U.S.A.BargainBookStores
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Paperback or Softback. Condizione: New. Reinforcement Learning for Finance: Solve Problems in Finance with CNN and Rnn Using the Tensorflow Library. Book.

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- Prima edizione
Da: Rarewaves.com USA, London, LONDO, Regno UnitoRarewaves.com USA
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EUR 40,91
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Paperback. Condizione: New. 1st ed. 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.

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- Prima edizione
Da: Kennys Bookshop and Art Galleries Ltd., Galway, GY, IrlandaKennys Bookshop and Art Galleries Ltd.
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Condizione: New. 2022. 1st ed. paperback. . . . . .

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Da: Ria Christie Collections, Uxbridge, Regno UnitoRia Christie Collections
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Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
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Da: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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Da: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
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Da: Kennys Bookstore, Olney, MD, U.S.A.Kennys Bookstore
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Condizione: New. 2022. 1st ed. paperback. . . . . . Books ship from the US and Ireland.

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Da: Revaluation Books, Exeter, , Regno UnitoRevaluation Books
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Da: Chiron Media, Wallingford, , Regno UnitoChiron Media
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Da: Ria Christie Collections, Uxbridge, Regno UnitoRia Christie Collections
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Da: Books Puddle, New York, NY, U.S.A.Books Puddle
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- Brossura
- Prima edizione
Da: Rarewaves USA United, OSWEGO, IL, U.S.A.Rarewaves USA United
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Paperback. Condizione: New. 1st ed. 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.

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- Prima edizione
Da: AussieBookSeller, Truganina, VIC, AustraliaAussieBookSeller
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 61,32
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Paperback. Condizione: new. Paperback. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Altre immagini- Brossura
Da: preigu, Osnabrück, Germaniapreigu
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 36,90
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Taschenbuch. Condizione: Neu. Reinforcement Learning for Finance | Solve Problems in Finance with CNN and RNN Using the TensorFlow Library | Samit Ahlawat | Taschenbuch | xv | Englisch | 2022 | Apress | EAN 9781484288344 | Verantwortliche Person für die EU: APress in Springer Science + Business Media, Heidelberger Platz 3, 14197… Berlin, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

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- Prima edizione
Da: Rarewaves.com UK, London, Regno UnitoRarewaves.com UK
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 37,30
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Paperback. Condizione: New. 1st ed. 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.

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Da: Buchpark, Trebbin, , GermaniaBuchpark
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EUR 19,27
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Condizione: Gut. Zustand: Gut | Seiten: 440 | Sprache: Englisch | Produktart: Bücher | 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. Ne…xt, 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.

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Da: Buchpark, Trebbin, , GermaniaBuchpark
Contatta il venditoreVenditore con 5 stelleCondizione: Usato - Ottimo
EUR 19,85
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Condizione: Sehr gut. Zustand: Sehr gut | Seiten: 440 | Sprache: Englisch | Produktart: Bücher | 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 ne…tworks. 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.

- Brossura
Da: Buchpark, Trebbin, , GermaniaBuchpark
Contatta il venditoreVenditore con 5 stelleCondizione: Usato
EUR 20,44
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Condizione: Hervorragend. Zustand: Hervorragend | Seiten: 440 | Sprache: Englisch | Produktart: Bücher | 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 n…eural 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.

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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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EUR 68,63
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Taschenbuch. Condizione: Neu. Neuware.

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- Print on Demand
Da: Brook Bookstore On Demand, Napoli, NA, ItaliaBrook Bookstore On Demand
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EUR 39,22
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Condizione: new. Questo è un articolo print on demand.

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- Print on Demand
Da: Revaluation Books, Exeter, , Regno UnitoRevaluation Books
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 35,59
EUR 14,40 spedizioneSpedito da Regno Unito a U.S.A.Quantità: 1 disponibili
Paperback. Condizione: Brand New. 438 pages. 9.25x6.10x1.02 inches. In Stock. This item is printed on demand.