{"product_id":"probabilistic-deep-learning","title":"Probabilistic Deep Learning","description":"\u003cp\u003e\u003ci\u003e\u003cstrong\u003eProbabilistic Deep Learning\u003c\/strong\u003e\u003c\/i\u003e\u003cb\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\u003c\/p\u003e\u003cp\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.\u003c\/p\u003e\u003cp\u003ePurchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eAbout the technology\u003c\/b\u003e\u003cbr\u003eThe world is a noisy and uncertain place. Probabilistic deep learning 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.\u003c\/p\u003e\u003cp\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.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eWhat's inside\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003e    Explore maximum likelihood and the statistical basis of deep learning\u003cbr\u003e    Discover probabilistic models that can indicate possible outcomes\u003cbr\u003e    Learn to use normalizing flows for modeling and generating complex distributions\u003cbr\u003e    Use Bayesian neural networks to access the uncertainty in the model\u003c\/p\u003e\u003cp\u003e\u003cb\u003eAbout the reader\u003c\/b\u003e\u003cbr\u003eFor experienced machine learning developers.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003ePART 1 - BASICS OF DEEP LEARNING\u003c\/p\u003e\u003cp\u003e1 Introduction to probabilistic deep learning\u003c\/p\u003e\u003cp\u003e2 Neural network architectures\u003c\/p\u003e\u003cp\u003e3 Principles of curve fitting\u003c\/p\u003e\u003cp\u003ePART 2 - MAXIMUM LIKELIHOOD APPROACHES FOR PROBABILISTIC DL MODELS\u003c\/p\u003e\u003cp\u003e4 Building loss functions with the likelihood approach\u003c\/p\u003e\u003cp\u003e5 Probabilistic deep learning models with TensorFlow Probability\u003c\/p\u003e\u003cp\u003e6 Probabilistic deep learning models in the wild\u003c\/p\u003e\u003cp\u003ePART 3 - BAYESIAN APPROACHES FOR PROBABILISTIC DL MODELS\u003c\/p\u003e\u003cp\u003e7 Bayesian learning\u003c\/p\u003e\u003cp\u003e8 Bayesian neural networks\u003c\/p\u003e\u003cp\u003e\u003cspan style=\"color: rgb(34, 34, 34); font-size: 24px; font-weight: 700;\"\u003eAbout the Author\u003c\/span\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eOliver Duerr\u003c\/b\u003e is professor for data science at the University of Applied Sciences in Konstanz, Germany.\u003c\/p\u003e\u003cp\u003e\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, and as a lecturer at ETH Zurich. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eElvis Murina\u003c\/b\u003e is a research assistant, responsible for the extensive exercises that accompany this book.\u003c\/p\u003e\u003cp\u003eDuerr and Sick are both experts in machine learning and statistics. They have supervised numerous bachelors, masters, and PhD theses on the topic of deep learning, and planned and conducted several postgraduate and masters- level deep learning courses. All three authors have been working with deep learning methods since 2013 and have extensive experience in both teaching the topic and developing probabilistic deep learning models.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eEbook License\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eEnd-User Warranty And License Agreement\u003c\/p\u003e\u003cp\u003e1. Grant Of License\u003c\/p\u003e\u003cp\u003eManning Has Authorized The Download By You Of An Unrestricted Number Of Copies Of The Electronic Book (Ebook) In Any Of The Available Formats. Manning Grants You A Nonexclusive, Nontransferable License To Use The Ebook According To The Terms And Conditions Herein. This License Agreement Permits You To Install The Ebook On Any And All Your Devices For Your Personal Use Only.\u003c\/p\u003e\u003cp\u003e2. Restrictions\u003c\/p\u003e\u003cp\u003eYou Shall Not: (1) Share, Resell, Rent, Assign, Timeshare, Distribute, Or Transfer All Or Part Of The Ebook Or Any Rights Granted Hereunder To Any Other Person; (2) Duplicate The Ebook, Except For A Single Backup Or Archival Copy; (3) Remove Any Proprietary Notices, Labels, Or Marks From The Ebook; (4) Transfer Or Sublicense Title To The Ebook To Any Other Party.\u003c\/p\u003e\u003cp\u003e3. Intellectual Property Protection\u003c\/p\u003e\u003cp\u003eThe Ebook Is Owned By Manning And Is Protected By United States And International Copyright And Other Intellectual Property Laws. Manning Reserves All Rights In The Ebook Not Expressly Granted Herein. This License And Your Right To Use The Ebook Terminate Automatically If You Violate Any Part Of This Agreement. In The Event Of Termination, You Must Remove The Original And Any Copies Of The Ebook From All Your Devices.\u003c\/p\u003e\u003cp\u003e4. 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Manning Does Not Warrant That The Operation Of The Ebook Will Be Uninterrupted Or Error Free. If The Ebook Was Purchased In The United States, The Above Exclusions May Not Apply To You As Some States Do Not Allow The Exclusion Of Implied Warranties. In Addition To The Above Warranty Rights, You May Also Have Other Rights That Vary From State To State.\u003c\/p\u003e\u003cp\u003e6. Limitation Of Liability\u003c\/p\u003e\u003cp\u003eIn No Event Will Manning Be Liable For Any Damages, Whether Arising For Tort Or Contract, Including Loss Of Data, Lost Profits, Or Other Special, Incidental, Consequential, Or Indirect Damages Arising Out Of The Use Or Inability To Use The Ebook.\u003c\/p\u003e\u003cp\u003e7. General\u003c\/p\u003e\u003cp\u003eThis Agreement Constitutes The Entire Agreement Between You And Manning And Supersedes Any Prior Agreement Concerning The Ebook. This Agreement Is Governed By The Laws Of The State Of New York.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e","brand":"Manning Publications","offers":[{"title":"Paperback","offer_id":50506285187142,"sku":"9781617296079","price":4432.0,"currency_code":"INR","in_stock":true},{"title":"E-Book","offer_id":50506285219910,"sku":"9781617296079-2","price":4333.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0754\/2797\/1142\/files\/51pN0P7n8uL._SX396_BO1_204_203_200.jpg?v=1790325284","url":"https:\/\/cb-india.com\/products\/probabilistic-deep-learning","provider":"Computer Bookshop (India) Pvt Ltd","version":"1.0","type":"link"}