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
For the first time in human history, the majority of the world's population lives in cities rather than rural areas. Whereas in industrialized countries urban and transport development has now reached a certain degree of saturation, it is proceeding in other regions of the world with an enormous dynamism. This book presents for the first time a survey of global urban and transport development in order to gain an overview of the magnitude of the global challenges. Against this background, the study proposes a direction for future deliberations that will provide an adequate response to the looming urban mobility problems. (Series: Mobility and Society / Mobilit�¤t und Gesellschaft, Vol. 9) [Subject: Sociology, Urban Studies, Transportation, Public Policy]
SEXUAL OFFENDING inASIA AUTHORITATIVE AND THOUGHT-PROVOKING WORK DETAILING THE PSYCHO-CRIMINOLOGICAL FACTORS INFLUENCING AND AFFECTING SEXUAL OFFENDERS IN ASIA Sexual Offending in Asia examines sexual offending from a general Asian perspective with a psycho-criminological approach (i.e., personal, social, and environmental mechanisms) to provide comprehensive coverage of different topics from both theoretical and practical (i.e., practice and policy) standpoints. This book is part of The Wiley Series in the Psycho-Criminology of Crime, Mental Health, and the Law, which aims to publish original, high-quality authored and edited collections on all aspects of crime, mental health, and the law from a psycho-criminological perspective. Sexual Offending in Asia is divided into two main sections—i.e., Part 1: Theories of Sexual Offending and Part 2: Sexual Offending in Asia—with five chapters in each section. In the second section, each chapter concludes with two case examples to illustrate the sexual offending phenomenon of each geographical location. Written by an award-winning author with significant experience in the field, Sexual Offending in Asia provides coverage of topics such as: Multi-level theories of general sexual offending, including multi-factorial (Level 1), single factor (Level ll), and micro-level or offense process (Level lll) theories of sexual offending for specific sex offender populations, including female sex offenders and sexual homicide offenders Sexual offending in Asia is discussed based on 5 geographical regions, namely East Asia, Southeast Asia, South Asia, West Asia, and Central Asia. Sexual offending in each geographical region is discussed comprehensively, including the prevalence and nature of sexual offending; cultural values and norms related to sexual offending; offender, victim, and offense characteristics; penal codes; and case examples Sexual Offending in Asia will be of immense interest not only to researchers and field practitioners whose work brings them into contact with sexual offenders, but more specifically to those who wish for an informed and informative understanding of Asian sexual offending regarding prevention and intervention strategies.
Marketing: A Relationship Perspective is back for a second edition and continues to set a benchmark for achievement in introductory marketing courses across Europe. It is a comprehensive, broad-based, and challenging basic marketing text, which describes and analyzes the basic concepts and strategic role of marketing and its practical application in managerial decision-making. It integrates the 'new' relationship approach into the traditional process of developing effective marketing plans. The book's structure fits to the marketing planning process of a company. Consequently, the book looks at the marketing management process from the perspective of both relational and transactional approach, suggesting that a company should, in any case, pursue an integrative and situational marketing management approach. Svend Hollensen's and Marc Opresnik's holistic approach covers both principles and practices, is drawn in equal measure from research and application, and is an ideal text for students, researchers, and practitioners alike.PowerPoint slides are available for all instructors who adopt this book as a course text.
In Hindu India both orality and sonality have enjoyed great cultural significance since earliest times. They have a distinct influence on how people approach texts. The importance of sound and its perception has led to rites, models of cosmic order, and abstract formulas. Sound serves both to stimulate religious feelings and to give them a sensory form. Starting from the perception and interpretation of sound, the authors chart an unorthodox cultural history of India, turning their attention to an important, but often neglected aspect of daily religious life. They provide a stimulating contribution to the study of cultural systems of perception that also adds new aspects to the debate on orality and literality.
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
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