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"A ... new philosophy and ... guide to getting the most out of your money--and out of life--for those who value memorable experiences as much as their earnings"--
Buy now to get the main key ideas from Bill Perkins's Die With Zero Many people work hard to build a fortune that they never get to spend and die with too much money left behind. In Die With Zero (2020), entrepreneur Bill Perkins offers a new approach. Perkins argues that you should live your life to the fullest, making money and spending most or even all of it before you die. You should invest in life’s real currency: peak experiences that stay with you until your deathbed. Perkins offers both a philosophy and a guide to implement it.
The book proffers solution to the scourge of unemployment. It is written to give succor to the unemployed who desires to start a job but for dearth of capital. It contains EIGHT major ways to source for funds without borrowing. A must read and the inevitable item in libraries!
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
This fourth volume of the landmark handbook focuses on the design, testing, and thermal management of 3D-integrated circuits, both from a technological and materials science perspective. Edited and authored by key contributors from top research institutions and high-tech companies, the first part of the book provides an overview of the latest developments in 3D chip design, including challenges and opportunities. The second part focuses on the test methods used to assess the quality and reliability of the 3D-integrated circuits, while the third and final part deals with thermal management and advanced cooling technologies and their integration.
Troubleshooting extrusion problems is one of the most challenging tasks in extrusion operations, requiring a good understanding of the extrusion process and the material properties, good instrumentation, good analysis tools, and a systematic and logical approach. This book addresses all issues crucial in extrusion troubleshooting. Additionally, it includes industrial case studies, richly illustrated with photographs and photomicrographs, used to provide exemplary approaches to efficient problem analysis and problem solving. The interconnectivity between the different relevant knowledge areas such as materials engineering, processing technology, and product development is emphasized. This revised third edition comprises a very significant update, with a great deal of new content, especially focusing on additional case studies as well as new sections on collection and interpretation of extrusion process data, rotational rheometry, the smartphone, how screw design can affect extruder performance, melt temperature variation, recent research on automatic optimization of extruder barrel temperatures, process signal analysis using Fast Fourier Transform, among other topics.
Exploring the ethical questions posed by, in, and about children’s literature, this collection examines the way texts intended for children raise questions of value, depict the moral development of their characters, and call into attention shared moral presuppositions. The essays in Part I look at various past attempts at conveying moral messages to children and interrogate their underlying assumptions. What visions of childhood were conveyed by explicit attempts to cultivate specific virtues in children? What unstated cultural assumptions were expressed by growing resistance to didacticism? How should we prepare children to respond to racism in their books and in their society? Part II takes up the ethical orientations of various classic and contemporary texts, including 'prosaic ethics' in the Hundred Acre Wood, moral discernment in Narnia, ethical recognition in the distant worlds traversed by L’Engle, and virtuous transgression in recent Anglo-American children’s literature and in the emerging children’s literature of 1960s Taiwan. Part III’s essays engage in ethical criticism of arguably problematic messages about our relationship to nonhuman animals, about war, and about prejudice. The final section considers how we respond to children’s literature with ethically focused essays exploring a range of ways in which child readers and adult authorities react to children’s literature. Even as children’s literature has evolved in opposition to its origins in didactic Sunday school tracts and moralizing fables, authors, parents, librarians, and scholars remain sensitive to the values conveyed to children through the texts they choose to share with them.
In the world today, there is a Luciferian Occult Epistemological Autocracy where ÒTruthÓ is allocated to a few Luciferian Elite Scientism Priests. There is a self-professed superiority amongst these hypnotized pseudo-academics, drunk on blind ambition, and hell-bent on creating a worldwide Techno Mystical Technocracy. In their eyes, they should be the supreme rulers over all others in a sterile, Brave New World, digitally manipulated and controlled tyranny. Where any sane and seasoned thinker would see inherent limitations to what Mankind can know and perceive, there are no such intrinsic limitations of empirical or epistemic reasoning within this Cult of The Luciferian Elite. This riveting book explains how this all happened, why, and what the chilling conclusion will be for Mankind as it endures the One Deception to Rule Them All.
This book constitutes the refereed proceedings of the 9th International Conference on Artificial General Intelligence, AGI 2016, held in New York City, NY, USA, in July 2016 as part of HLAI 2016, the Joint Multi-Conference on Human-Level Artificial Intelligence 2016. The 24 full papers, 2 short papers, and 10 poster papers presented were carefully reviewed and selected from 67 submissions. AGI research differs from the ordinary AI research by stressing on the versatility and wholeness of intelligence, and by carrying out the engineering practice according to an outline of a system comparable to the human mind inSelf a certain sense.
