Ahlawat samit (39 resultados)

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Paperback or Softback. Condición: New. Reinforcement Learning for Finance: Solve Problems in Finance with CNN and Rnn Using the Tensorflow Library. Book.

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Paperback. Condición: 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.…

Paperback. Condición: new. Paperback. Statistical quantitative methods are vital for financial valuation models and benchmarking machine learning models in finance.This book explores the theoretical foundations of statistical models, from ordinary least squares (OLS) to the generalized method of moments (GMM) used in econometrics. It enriches your understanding through practical examples drawn from applied finance, demonstrating the real-world applications of these concepts. Additionally, the book delves into non-linear methods and Bayesian approaches, which are becoming increasingly popular among practitioners thanks to advancements in computational resources. By mastering these topics, you will be equipped to build foundational models crucial for applied data science, a skill highly sought after by software engineering and asset management firms. The book also offers valuable insights into quantitative portfolio management, showcasing how traditional data science tools can be enhanced with machine learning models. These enhancements are illustrated through real-world examples from finance and econometrics, accompanied by Python code. This practical approach ensures that you can apply what you learn, gaining proficiency in the statsmodels library and becoming adept at designing, implementing, and calibrating your models.By understanding and applying these statistical models, you enhance your data science skills and effectively tackle financial challenges. What You Will LearnUnderstand the fundamentals of linear regression and its applications in financial data analysis and predictionApply generalized linear models for handling various types of data distributions and enhancing model flexibilityGain insights into regime switching models to capture different market conditions and improve financial forecastingBenchmark machine learning models against traditional statistical methods to ensure robustness and reliability in financial applications Who This Book Is ForData scientists, machine learning engineers, finance professionals, and software engineers Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condición: New. 2022. 1st ed. paperback. . . . . .

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Condición: New. 2022. 1st ed. paperback. . . . . . Books ship from the US and Ireland.

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Paperback. Condición: Brand New. 438 pages. 9.25x6.10x1.02 inches. In Stock.

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Condición: New. 1st ed. edition NO-PA16APR2015-KAP.

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Paperback. Condición: 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.…

Taschenbuch. Condición: Neu. Neuware.

Taschenbuch. Condición: 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.…

Condición: 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. 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.…

Condición: 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 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.…

Condición: 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 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.…

Condición: new. Questo è un articolo print on demand.

Condición: new. Questo è un articolo print on demand.

Paperback. Condición: Brand New. 438 pages. 9.25x6.10x1.02 inches. In Stock. This item is printed on demand.

PAP. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 440 pp. Englisch.…

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Condición: New. PRINT ON DEMAND.