{"product_id":"reinforcement-learning-for-finance-solve-problems-in-finance-with-cnn-and-rnn-using-the-tensorflow-library-paperback","title":"Reinforcement Learning for Finance: Solve Problems in Finance with CNN and Rnn Using the Tensorflow Library - Paperback","description":"\u003cp\u003eby \u003cb\u003eSamit Ahlawat\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003eThis book introduces reinforcement learning with mathematical theory and practical examples from quantitative finance using the TensorFlow library.\u003cbr\u003e\u003ci\u003eReinforcement Learning for Finance\u003c\/i\u003e 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, and loss functions.\u003cbr\u003eAfter completing this book, you will understand reinforcement learning with deep q and generative adversarial networks using the TensorFlow library.\u003cbr\u003e\u003cb\u003eWhat You Will Learn\u003c\/b\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eUnderstand the fundamentals of reinforcement learning\u003c\/li\u003e\n\u003cli\u003eApply reinforcement learning programming techniques to solve quantitative-finance problems\u003c\/li\u003e\n\u003cli\u003eGain insight into convolutional neural networks and recurrent neural networks\u003c\/li\u003e\n\u003cli\u003eUnderstand the Markov decision process\u003c\/li\u003e\n\u003c\/ul\u003e\u003cbr\u003e\u003cb\u003eWho This Book Is For\u003c\/b\u003eData Scientists, Machine Learning engineers and Python programmers who want to apply reinforcement learning to solve problems.\u003cbr\u003e\u003ch3\u003eBack Jacket\u003c\/h3\u003e\u003cp\u003eThis book introduces reinforcement learning with mathematical theory and practical examples from quantitative finance using the TensorFlow library.\u003cbr\u003e\u003ci\u003eReinforcement Learning for Finance\u003c\/i\u003e 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, and loss functions.\u003cbr\u003eAfter completing this book, you will understand reinforcement learning with deep q and generative adversarial networks using the TensorFlow library.\u003cbr\u003e\u003cb\u003eWhat You Will Learn\u003c\/b\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eUnderstand the fundamentals of reinforcement learning\u003c\/li\u003e\n\u003cli\u003eApply reinforcement learning programming techniques to solve quantitative-finance problems\u003c\/li\u003e\n\u003cli\u003eGain insight into convolutional neural networks and recurrent neural networks\u003c\/li\u003e\n\u003cli\u003eUnderstand the Markov decision process\u003c\/li\u003e\n\u003c\/ul\u003e\u003cbr\u003e\u003cb\u003eWho This Book Is For\u003c\/b\u003eData Scientists, Machine Learning engineers and Python programmers who want to apply reinforcement learning to solve problems.\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003eSamit Ahlawat is a Senior Vice President in Quantitative Research, Capital Modeling at J.P. Morgan Chase in New York, US. In his current role, he is responsible for building trading strategies for asset management and for building risk management models. His research interests include artificial intelligence, risk management and algorithmic trading strategies. He has given CQF institute talks on artificial intelligence, has authored several research papers in finance and holds a patent for facial recognition technology. In his spare time, he contributes to open source code.\u003c\/p\u003e\u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 423\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.89 x 9.21 x 6.14 IN\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eIllustrated:\u003c\/strong\u003e Yes\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e December 27, 2022\u003c\/div\u003e","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":51783034143008,"sku":"9781484288344","price":41.02,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0974\/9764\/5344\/files\/c9bf8d8993fd48e0893ba96bfadb6a93.webp?v=1780578404","url":"https:\/\/ebocreations.com\/products\/reinforcement-learning-for-finance-solve-problems-in-finance-with-cnn-and-rnn-using-the-tensorflow-library-paperback","provider":"The E-Book Oasis LLC","version":"1.0","type":"link"}