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Author: Scott Huler
Publisher: Rodale
Published: 2010-05-11
Total Pages: 258
ISBN-13: 1605296473
DOWNLOAD EBOOKInvestigates the systems of infrastructure that sustain the world and the cultures of historical periods, following various elements, from electricity and pavement to water and waste disposal, back to their origins and people who operate them.
Author: David Hatcher Childress
Publisher: Adventures Unlimited Press
Published: 1987
Total Pages: 286
ISBN-13: 9780932813039
DOWNLOAD EBOOKLearn the purpose of ley lines and ancient megalithic structures located on the grid. Discover how the grid made the Philadelphia Experiment possible. Explore Coral Castle and other mysteries including acoustic levitation, Tesla shields and Scalar wave weaponry.
Author: Phillip F. Schewe
Publisher: National Academies Press
Published: 2007-02-20
Total Pages: 319
ISBN-13: 030910260X
DOWNLOAD EBOOKThe electrical grid goes everywhere-it's the largest and most complex machine ever made. Yet the system is built in such a way that the bigger it gets, the more inevitable its collapse. Named the greatest engineering achievement of the 20th century by the National Academy of Engineering, the electrical grid is the largest industrial investment in the history of humankind. It reaches into your home, snakes its way to your bedroom, and climbs right up into the lamp next to your pillow. At times, it almost seems alive, like some enormous circulatory system that pumps life to big cities and the most remote rural areas. Constructed of intricately interdependent components, the grid operates on a rapidly shrinking margin for error. Things can-and do-go wrong in this system, no matter how many preventive steps we take. Just look at the colossal 2003 blackout, when 50 million Americans lost power due to a simple error at a power plant in Ohio; or the one a month later, which blacked out 57 million Italians. And these two combined don't even compare to the 2001 outage in India, which affected 226 million people. The Grid is the first history of the electrical grid intended for general readers, and it comes at a time when we badly need such a guide. As we get more and more dependent on electricity to perform even the most mundane daily tasks, the grid's inevitable shortcomings will take a toll on populations around the globe. At a moment when energy issues loom large on the nation's agenda and our hunger for electricity grows, The Grid is as timely as it is compelling.
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Total Pages: 418
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DOWNLOAD EBOOKAuthor: M. A. Shea
Publisher:
Published: 1976
Total Pages: 102
ISBN-13:
DOWNLOAD EBOOKTables of cosmic-ray cutoff rigidities using the trajectory-tracing technique are given for five Epochs of the geomagnetic field. These values have been determined utilizing the International Geomagnetic Reference Field with time derivatives applied so that the coefficients for the field model are appropriate for the following Epochs: 1955.0, 1965.0, 1966.5, 1970.0, and 1975.0. Each table includes the geographic coordinates and L value of each location together with the main cutoff rigidity, the Stormer cutoff rigidity, and the effective cutoff rigidity. Tables for both vertical and non-vertical directions are included, as well as a listing of the FORTRAN computer program used to calculate these values. (Author).
Author: Thanos Vasilakos
Publisher: Springer
Published: 2010-11-29
Total Pages: 486
ISBN-13: 3642176046
DOWNLOAD EBOOKWelcome to the proceedings of the 2010 International Conference on Future Gene- tion Communication and Networking (FGCN 2010) – one of the partnering events of the Second International Mega-Conference on Future Generation Information Technology (FGIT 2010). FGCN brings together researchers from academia and industry as well as practit- ners to share ideas, problems and solutions relating to the multifaceted aspects of communication and networking, including their links to computational sciences, mathematics and information technology. In total, 1,630 papers were submitted to FGIT 2010 from 30 countries, which - cludes 150 papers submitted to the FGCN 2010 Special Sessions. The submitted papers went through a rigorous reviewing process: 395 of the 1,630 papers were - cepted for FGIT 2010, while 70 papers were accepted for the FGCN 2010 Special Sessions. Of the 70 papers, 6 were selected for the special FGIT 2010 volume p- lished by Springer in LNCS series. Fifty-one papers are published in this volume, and 13 papers were withdrawn due to technical reasons. We would like to acknowledge the great effort of the FGCN 2010 International Advisory Board and Special Session Co-chairs, as well as all the organizations and individuals who supported the idea of publishing this volume of proceedings, incl- ing SERSC and Springer. Also, the success of the conference would not have been possible without the huge support from our sponsors and the work of the Organizing Committee.
Author: Mohit Sewak
Publisher: Springer
Published: 2019-06-27
Total Pages: 215
ISBN-13: 9811382859
DOWNLOAD EBOOKThis book starts by presenting the basics of reinforcement learning using highly intuitive and easy-to-understand examples and applications, and then introduces the cutting-edge research advances that make reinforcement learning capable of out-performing most state-of-art systems, and even humans in a number of applications. The book not only equips readers with an understanding of multiple advanced and innovative algorithms, but also prepares them to implement systems such as those created by Google Deep Mind in actual code. This book is intended for readers who want to both understand and apply advanced concepts in a field that combines the best of two worlds – deep learning and reinforcement learning – to tap the potential of ‘advanced artificial intelligence’ for creating real-world applications and game-winning algorithms.
Author: Sayon Dutta
Publisher: Packt Publishing Ltd
Published: 2018-04-24
Total Pages: 327
ISBN-13: 1788830717
DOWNLOAD EBOOKLeverage the power of the Reinforcement Learning techniques to develop self-learning systems using Tensorflow Key Features Learn reinforcement learning concepts and their implementation using TensorFlow Discover different problem-solving methods for Reinforcement Learning Apply reinforcement learning for autonomous driving cars, robobrokers, and more Book Description Reinforcement Learning (RL), allows you to develop smart, quick and self-learning systems in your business surroundings. It is an effective method to train your learning agents and solve a variety of problems in Artificial Intelligence—from games, self-driving cars and robots to enterprise applications that range from datacenter energy saving (cooling data centers) to smart warehousing solutions. The book covers the major advancements and successes achieved in deep reinforcement learning by synergizing deep neural network architectures with reinforcement learning. The book also introduces readers to the concept of Reinforcement Learning, its advantages and why it’s gaining so much popularity. The book also discusses on MDPs, Monte Carlo tree searches, dynamic programming such as policy and value iteration, temporal difference learning such as Q-learning and SARSA. You will use TensorFlow and OpenAI Gym to build simple neural network models that learn from their own actions. You will also see how reinforcement learning algorithms play a role in games, image processing and NLP. By the end of this book, you will have a firm understanding of what reinforcement learning is and how to put your knowledge to practical use by leveraging the power of TensorFlow and OpenAI Gym. What you will learn Implement state-of-the-art Reinforcement Learning algorithms from the basics Discover various techniques of Reinforcement Learning such as MDP, Q Learning and more Learn the applications of Reinforcement Learning in advertisement, image processing, and NLP Teach a Reinforcement Learning model to play a game using TensorFlow and the OpenAI gym Understand how Reinforcement Learning Applications are used in robotics Who this book is for If you want to get started with reinforcement learning using TensorFlow in the most practical way, this book will be a useful resource. The book assumes prior knowledge of machine learning and neural network programming concepts, as well as some understanding of the TensorFlow framework. No previous experience with Reinforcement Learning is required.
Author: Fran Berman
Publisher: John Wiley and Sons
Published: 2003-04-18
Total Pages: 1076
ISBN-13: 9780470853191
DOWNLOAD EBOOKUnter "Grid Computing" versteht man die gleichzeitige Nutzung vieler Computer in einem Netzwerk für die Lösung eines einzelnen Problems. Grundsätzliche Aspekte und anwendungsbezogene Details zu diesem Gebiet finden Sie in diesem Band. - Grid Computing ist ein viel versprechender Trend, denn man kann damit (1) vorhandene Computer-Ressourcen kosteneffizient nutzen, (2) Probleme lösen, für die enorme Rechenleistungen erforderlich sind, und (3) Synergieeffekte erzielen, auch im globalen Maßstab - Ansatz ist in Forschung und Industrie (IBM, Sun, HP und andere) zunehmend populär (aktuelles Beispiel: Genomforschung) - Buch deckt Motivationen zur Einführung von Grids ebenso ab wie technologische Grundlagen und ausgewählte Beispiele für moderne Anwendungen
Author: Alexander Zai
Publisher: Manning
Published: 2020-04-28
Total Pages: 381
ISBN-13: 1617295434
DOWNLOAD EBOOKSummary Humans learn best from feedback—we are encouraged to take actions that lead to positive results while deterred by decisions with negative consequences. This reinforcement process can be applied to computer programs allowing them to solve more complex problems that classical programming cannot. Deep Reinforcement Learning in Action teaches you the fundamental concepts and terminology of deep reinforcement learning, along with the practical skills and techniques you’ll need to implement it into your own projects. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Deep reinforcement learning AI systems rapidly adapt to new environments, a vast improvement over standard neural networks. A DRL agent learns like people do, taking in raw data such as sensor input and refining its responses and predictions through trial and error. About the book Deep Reinforcement Learning in Action teaches you how to program AI agents that adapt and improve based on direct feedback from their environment. In this example-rich tutorial, you’ll master foundational and advanced DRL techniques by taking on interesting challenges like navigating a maze and playing video games. Along the way, you’ll work with core algorithms, including deep Q-networks and policy gradients, along with industry-standard tools like PyTorch and OpenAI Gym. What's inside Building and training DRL networks The most popular DRL algorithms for learning and problem solving Evolutionary algorithms for curiosity and multi-agent learning All examples available as Jupyter Notebooks About the reader For readers with intermediate skills in Python and deep learning. About the author Alexander Zai is a machine learning engineer at Amazon AI. Brandon Brown is a machine learning and data analysis blogger. Table of Contents PART 1 - FOUNDATIONS 1. What is reinforcement learning? 2. Modeling reinforcement learning problems: Markov decision processes 3. Predicting the best states and actions: Deep Q-networks 4. Learning to pick the best policy: Policy gradient methods 5. Tackling more complex problems with actor-critic methods PART 2 - ABOVE AND BEYOND 6. Alternative optimization methods: Evolutionary algorithms 7. Distributional DQN: Getting the full story 8.Curiosity-driven exploration 9. Multi-agent reinforcement learning 10. Interpretable reinforcement learning: Attention and relational models 11. In conclusion: A review and roadmap
