{"product_id":"9781617296079","title":"Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability","description":"\u003ctable\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eAuthor\/Contributor(s):\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"\"\u003eDuerr, Oliver; Sick, Beate; Murina, Elvis\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003ePublisher:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eManning\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eDate:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e11\/10\/2020\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eBinding:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"\"\u003ePaperback\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eCondition:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"\"\u003eNEW\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\u003cb\u003e\u003ci\u003eProbabilistic Deep Learning\u003c\/i\u003e is a hands-on guide to the principles that support neural networks. Learn to improve network performance with the right distribution for different data types, and discover Bayesian variants that can state their own uncertainty to increase accuracy. This book provides easy-to-apply code and uses popular frameworks to keep you focused on practical applications.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eSummary\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eProbabilistic Deep Learning: With Python, Keras and TensorFlow Probability\u003c\/i\u003e teaches the increasingly popular probabilistic approach to deep learning that allows you to refine your results more quickly and accurately without much trial-and-error testing. Emphasizing practical techniques that use the Python-based Tensorflow Probability Framework, you’ll learn to build highly-performant deep learning applications that can reliably handle the noise and uncertainty of real-world data.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the technology\u003c\/b\u003e\u003cbr\u003eThe world is a noisy and uncertain place. \u003ci\u003eProbabilistic deep learning\u003c\/i\u003e models capture that noise and uncertainty, pulling it into real-world scenarios. Crucial for self-driving cars and scientific testing, these techniques help deep learning engineers assess the accuracy of their results, spot errors, and improve their understanding of how algorithms work.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the book\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eProbabilistic Deep Learning\u003c\/i\u003e is a hands-on guide to the principles that support neural networks. Learn to improve network performance with the right distribution for different data types, and discover Bayesian variants that can state their own uncertainty to increase accuracy. This book provides easy-to-apply code and uses popular frameworks to keep you focused on practical applications.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eWhat's inside\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eExplore maximum likelihood and the statistical basis of deep learning\u003cbr\u003eDiscover probabilistic models that can indicate possible outcomes\u003cbr\u003eLearn to use normalizing flows for modeling and generating complex distributions\u003cbr\u003eUse Bayesian neural networks to access the uncertainty in the model\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the reader\u003c\/b\u003e\u003cbr\u003eFor experienced machine learning developers.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the author\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eOliver Dürr\u003c\/b\u003e is a professor at the University of Applied Sciences in Konstanz, Germany. \u003cb\u003eBeate Sick\u003c\/b\u003e holds a chair for applied statistics at ZHAW and works as a researcher and lecturer at the University of Zurich. \u003cb\u003eElvis Murina\u003c\/b\u003e is a data scientist.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003ePART 1 - BASICS OF DEEP LEARNING\u003cbr\u003e\u003cbr\u003e1 Introduction to \u003ci\u003eprobabilistic deep learning\u003c\/i\u003e\u003cbr\u003e\u003cbr\u003e2 Neural network architectures\u003cbr\u003e\u003cbr\u003e3 Principles of curve fitting\u003cbr\u003e\u003cbr\u003ePART 2 - MAXIMUM LIKELIHOOD APPROACHES FOR PROBABILISTIC DL MODELS\u003cbr\u003e\u003cbr\u003e4 Building loss functions with the likelihood approach\u003cbr\u003e\u003cbr\u003e5 \u003ci\u003eProbabilistic deep learning\u003c\/i\u003e models with TensorFlow Probability\u003cbr\u003e\u003cbr\u003e6 \u003ci\u003eProbabilistic deep learning\u003c\/i\u003e models in the wild\u003cbr\u003e\u003cbr\u003ePART 3 - BAYESIAN APPROACHES FOR PROBABILISTIC DL MODELS\u003cbr\u003e\u003cbr\u003e7 Bayesian learning\u003cbr\u003e\u003cbr\u003e8 Bayesian neural networks","brand":"Manning","offers":[{"title":"Default Title","offer_id":42973608870143,"sku":"9781617296079","price":49.99,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0452\/0886\/2873\/files\/Jacket_d0c53847-0007-4345-b2d4-7e97dfe85282.jpg?v=1771351391","url":"https:\/\/massivebookshop.com\/products\/9781617296079","provider":"MASSIVE BOOKSHOP","version":"1.0","type":"link"}