Songlist - All Shook Up; Always On My Mind; An American Trilogy; Are You Lonesome Tonight?; A Big Hunk O' Love; Blue Christmas; Blue Suede Shoes; Bossa Nova Baby; Burning Love; Can't Help Falling In Love; Crying In The Chapel; Don't; Don't Be Cruel (To A Heart That's True); Don't Cry Daddy; Good Luck Charm; Hard Headed Woman; Heartbreak Hotel; His Latest Flame; Hound Dog; I Feel So Bad; I Forgot To Remember To Forget; I Got Stung; I Need Your Love Tonight; I Want You, I Need You, I Love You; I'm A Roustabout; If I Can Dream; In The Ghetto (The Vicious Circle); It's Now Or Never; Jailhouse Rock; Kentucky Rain; King Creole; Little Sister; Love Me; Love Me Tender; Loving You; Mean Woman Blues Memories; A Mess Of Blues; Moody Blue; One Night; Return To Sender; Rock-A-Hula Baby Rubberneckin'; She's Not You; Stuck On You; Surrender; Suspicious Minds; (Let Me Be Your) Teddy Bear; That's All Right; The Promised Land; Too Much; Treat Me Nice; Trouble; Viva Las Vegas; Way Down; Wear My Ring Around Your Neck; The Wonder Of You; You Don't Have To Say You Love Me; You're The Devil In Disguise.
Probabilistic Deep Learning 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. Summary Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability 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. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology The 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. About the book Probabilistic Deep Learning 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. What's inside Explore maximum likelihood and the statistical basis of deep learning Discover probabilistic models that can indicate possible outcomes Learn to use normalizing flows for modeling and generating complex distributions Use Bayesian neural networks to access the uncertainty in the model About the reader For experienced machine learning developers. About the author Oliver Dürr is a professor at the University of Applied Sciences in Konstanz, Germany. Beate Sick holds a chair for applied statistics at ZHAW and works as a researcher and lecturer at the University of Zurich. Elvis Murina is a data scientist. Table of Contents PART 1 - BASICS OF DEEP LEARNING 1 Introduction to probabilistic deep learning 2 Neural network architectures 3 Principles of curve fitting PART 2 - MAXIMUM LIKELIHOOD APPROACHES FOR PROBABILISTIC DL MODELS 4 Building loss functions with the likelihood approach 5 Probabilistic deep learning models with TensorFlow Probability 6 Probabilistic deep learning models in the wild PART 3 - BAYESIAN APPROACHES FOR PROBABILISTIC DL MODELS 7 Bayesian learning 8 Bayesian neural networks
(Pro Vocal). Whether you're a karaoke singer or preparing for an audition, the Pro Vocal series is for you. Each edition contains the lyrics, melody, and chord symbols for eight hit songs. The audio includes demos for listening and separate backing tracks so you can sing along. Perfect for home rehearsal, parties, auditions, corporate events, and gigs without a backup band. This volume includes 8 of the King's best: Blue Suede Shoes * Can't Help Falling in Love * Don't Be Cruel (To a Heart That's True) * Good Luck Charm * I Want You, I Need You, I Love You * Love Me * (Let Me Be Your) Teddy Bear * Treat Me Nice.
Probabilistic Deep Learning 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. Summary Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability 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. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology The 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. About the book Probabilistic Deep Learning 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. What's inside Explore maximum likelihood and the statistical basis of deep learning Discover probabilistic models that can indicate possible outcomes Learn to use normalizing flows for modeling and generating complex distributions Use Bayesian neural networks to access the uncertainty in the model About the reader For experienced machine learning developers. About the author Oliver Dürr is a professor at the University of Applied Sciences in Konstanz, Germany. Beate Sick holds a chair for applied statistics at ZHAW and works as a researcher and lecturer at the University of Zurich. Elvis Murina is a data scientist. Table of Contents PART 1 - BASICS OF DEEP LEARNING 1 Introduction to probabilistic deep learning 2 Neural network architectures 3 Principles of curve fitting PART 2 - MAXIMUM LIKELIHOOD APPROACHES FOR PROBABILISTIC DL MODELS 4 Building loss functions with the likelihood approach 5 Probabilistic deep learning models with TensorFlow Probability 6 Probabilistic deep learning models in the wild PART 3 - BAYESIAN APPROACHES FOR PROBABILISTIC DL MODELS 7 Bayesian learning 8 Bayesian neural networks
AS SEEN IN USA TODAY, WIRED, YAHOO! AND MORE. "When I was a child, ladies and gentlemen, I was a dreamer. I read comic books, and I was the hero of the comic book...So every dream that I¡¯ve dreamed has come true a hundred times" -Elvis This Free Comic Book Day Preview of the Graphic Elvis collector¡¯s book, features selected stories and excerpts from the deluxe hardcover edition of the book. "Graphic Elvis" is an illustrated homage to Elvis¡¯ lifelong appreciation of comic books, commemorating the 35th anniversary of Elvis Presley¡¯s death in 2012. In the same way comic books inspired Elvis, this book allowed today¡¯s premiere comic book creators to find inspiration from Elvis¡¯ treasured archives at Graceland, creating a unique visual experience for his millions of fans. Acclaimed graphic novel artists recruited from around the world portray the King of Rock 'n¡¯ Roll in unprecedented visual styles. Beyond the original illustrations, the book features numerous hand-written notes and musings, rarely seen by the public and written in the margins of various books owned by Elvis. A whole new way to experience the greatest rockstar the world has ever known.
(Music Minus One). All that's missing are your vocals! 10 timeless classics from "The King" Sheeran are presented in this book which includes the lyrics, vocal lines and piano accompaniments for each song, plus online audio tracks containing demos for listening, and separate backing tracks so you can sing along. Songs include: All Shook Up * Blue Suede Shoes * Can't Help Falling in Love * Don't Be Cruel (To a Heart That's True) * Hound Dog * It's Now or Never * Jailhouse Rock * Love Me Tender * Return to Sender * Suspicious Minds.
Elvis was the king of rock 'n' roll . . . and with his bellbottom pants and sequined capes, he always looked the part. This outrageously fun kit includes an Elvis figure, plastic display base, eight possible outfits, and a booklet tracing the style icon's look from the 1950s through the late 1970s, ranging from film costumes to the famous Vegas glitter jumpsuit. Viva Las Vegas! Elvis and Elvis Presley are registered trademarks of Elvis Presley Enterprises, Inc. (c) 2005 EPE.
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