Download or read book Learning Deep Architectures for AI written by Yoshua Bengio and published by Now Publishers Inc. This book was released on 2009 with total page 145 pages. Available in PDF, EPUB and Kindle. Book excerpt: Theoretical results suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g. in vision, language, and other AI-level tasks), one may need deep architectures. Deep architectures are composed of multiple levels of non-linear operations, such as in neural nets with many hidden layers or in complicated propositional formulae re-using many sub-formulae. Searching the parameter space of deep architectures is a difficult task, but learning algorithms such as those for Deep Belief Networks have recently been proposed to tackle this problem with notable success, beating the state-of-the-art in certain areas. This paper discusses the motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer models such as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks.

Download or read book Handbook of Economic Forecasting written by Graham Elliott and published by Elsevier. This book was released on 2013-08-23 with total page 667 pages. Available in PDF, EPUB and Kindle. Book excerpt: The highly prized ability to make financial plans with some certainty about the future comes from the core fields of economics. In recent years the availability of more data, analytical tools of greater precision, and ex post studies of business decisions have increased demand for information about economic forecasting. Volumes 2A and 2B, which follows Nobel laureate Clive Granger's Volume 1 (2006), concentrate on two major subjects. Volume 2A covers innovations in methodologies, specifically macroforecasting and forecasting financial variables. Volume 2B investigates commercial applications, with sections on forecasters' objectives and methodologies. Experts provide surveys of a large range of literature scattered across applied and theoretical statistics journals as well as econometrics and empirical economics journals. The Handbook of Economic Forecasting Volumes 2A and 2B provide a unique compilation of chapters giving a coherent overview of forecasting theory and applications in one place and with up-to-date accounts of all major conceptual issues. - Focuses on innovation in economic forecasting via industry applications - Presents coherent summaries of subjects in economic forecasting that stretch from methodologies to applications - Makes details about economic forecasting accessible to scholars in fields outside economics

Download or read book The Price of Oil written by Roberto F. Aguilera and published by Cambridge University Press. This book was released on 2016 with total page 253 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explains why oil prices rose so spectacularly in the past and examines how they will be suppressed in the future.

Download or read book Efficient Processing of Deep Neural Networks written by Vivienne Sze and published by Springer Nature. This book was released on 2022-05-31 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are currently widely used for many artificial intelligence (AI) applications, including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Therefore, techniques that enable efficient processing of deep neural networks to improve key metrics—such as energy-efficiency, throughput, and latency—without sacrificing accuracy or increasing hardware costs are critical to enabling the wide deployment of DNNs in AI systems. The book includes background on DNN processing; a description and taxonomy of hardware architectural approaches for designing DNN accelerators; key metrics for evaluating and comparing different designs; features of DNN processing that are amenable to hardware/algorithm co-design to improve energy efficiency and throughput; and opportunities for applying new technologies. Readers will find a structured introduction to the field as well as formalization and organization of key concepts from contemporary work that provide insights that may spark new ideas.

Download or read book The Distributional Implications of the Impact of Fuel Price Increases on Inflation written by Mr. Kangni R Kpodar and published by International Monetary Fund. This book was released on 2021-11-12 with total page 34 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper investigates the response of consumer price inflation to changes in domestic fuel prices, looking at the different categories of the overall consumer price index (CPI). We then combine household survey data with the CPI components to construct a CPI index for the poorest and richest income quintiles with the view to assess the distributional impact of the pass-through. To undertake this analysis, the paper provides an update to the Global Monthly Retail Fuel Price Database, expanding the product coverage to premium and regular fuels, the time dimension to December 2020, and the sample to 190 countries. Three key findings stand out. First, the response of inflation to gasoline price shocks is smaller, but more persistent and broad-based in developing economies than in advanced economies. Second, we show that past studies using crude oil prices instead of retail fuel prices to estimate the pass-through to inflation significantly underestimate it. Third, while the purchasing power of all households declines as fuel prices increase, the distributional impact is progressive. But the progressivity phases out within 6 months after the shock in advanced economies, whereas it persists beyond a year in developing countries.

Download or read book Oil Prices and the Global Economy written by Mr.Rabah Arezki and published by International Monetary Fund. This book was released on 2017-01-27 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper presents a simple macroeconomic model of the oil market. The model incorporates features of oil supply such as depletion, endogenous oil exploration and extraction, as well as features of oil demand such as the secular increase in demand from emerging-market economies, usage efficiency, and endogenous demand responses. The model provides, inter alia, a useful analytical framework to explore the effects of: a change in world GDP growth; a change in the efficiency of oil usage; and a change in the supply of oil. Notwithstanding that shale oil production today is more responsive to prices than conventional oil, our analysis suggests that an era of prolonged low oil prices is likely to be followed by a period where oil prices overshoot their long-term upward trend.

Download or read book International Energy Outlook written by and published by . This book was released on 1986 with total page 74 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Harmony Search and Nature Inspired Optimization Algorithms written by Neha Yadav and published by Springer. This book was released on 2018-08-23 with total page 1209 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book covers different aspects of real-world applications of optimization algorithms. It provides insights from the Fourth International Conference on Harmony Search, Soft Computing and Applications held at BML Munjal University, Gurgaon, India on February 7–9, 2018. It consists of research articles on novel and newly proposed optimization algorithms; the theoretical study of nature-inspired optimization algorithms; numerically established results of nature-inspired optimization algorithms; and real-world applications of optimization algorithms and synthetic benchmarking of optimization algorithms.

Download or read book Hubbert s Peak written by Kenneth S. Deffeyes and published by Princeton University Press. This book was released on 2008-09-29 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt: In 2001, Kenneth Deffeyes made a grim prediction: world oil production would reach a peak within the next decade--and there was nothing anyone could do to stop it. Deffeyes's claim echoed the work of geophysicist M. King Hubbert, who in 1956 predicted that U.S. oil production would reach its highest level in the early 1970s. Though roundly criticized by oil experts and economists, Hubbert's prediction came true in 1970. In this updated edition of Hubbert's Peak, Deffeyes explains the crisis that few now deny we are headed toward. Using geology and economics, he shows how everything from the rising price of groceries to the subprime mortgage crisis has been exacerbated by the shrinking supply--and growing price--of oil. Although there is no easy solution to these problems, Deffeyes argues that the first step is understanding the trouble that we are in.

Download or read book Exchange Rates and Oil Prices written by Robert A. Amano and published by . This book was released on 1995 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Non linear and Non stationary Time Series Analysis written by Maurice Bertram Priestley and published by . This book was released on 1988 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Numerical Computing with Python written by Pratap Dangeti and published by Packt Publishing Ltd. This book was released on 2018-12-21 with total page 676 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understand, explore, and effectively present data using the powerful data visualization techniques of Python Key FeaturesUse the power of Pandas and Matplotlib to easily solve data mining issuesUnderstand the basics of statistics to build powerful predictive data modelsGrasp data mining concepts with helpful use-cases and examplesBook Description Data mining, or parsing the data to extract useful insights, is a niche skill that can transform your career as a data scientist Python is a flexible programming language that is equipped with a strong suite of libraries and toolkits, and gives you the perfect platform to sift through your data and mine the insights you seek. This Learning Path is designed to familiarize you with the Python libraries and the underlying statistics that you need to get comfortable with data mining. You will learn how to use Pandas, Python's popular library to analyze different kinds of data, and leverage the power of Matplotlib to generate appealing and impressive visualizations for the insights you have derived. You will also explore different machine learning techniques and statistics that enable you to build powerful predictive models. By the end of this Learning Path, you will have the perfect foundation to take your data mining skills to the next level and set yourself on the path to become a sought-after data science professional. This Learning Path includes content from the following Packt products: Statistics for Machine Learning by Pratap DangetiMatplotlib 2.x By Example by Allen Yu, Claire Chung, Aldrin YimPandas Cookbook by Theodore PetrouWhat you will learnUnderstand the statistical fundamentals to build data modelsSplit data into independent groups Apply aggregations and transformations to each groupCreate impressive data visualizationsPrepare your data and design models Clean up data to ease data analysis and visualizationCreate insightful visualizations with Matplotlib and SeabornCustomize the model to suit your own predictive goalsWho this book is for If you want to learn how to use the many libraries of Python to extract impactful information from your data and present it as engaging visuals, then this is the ideal Learning Path for you. Some basic knowledge of Python is enough to get started with this Learning Path.

Download or read book Deep Learning written by Josh Patterson and published by "O'Reilly Media, Inc.". This book was released on 2017-07-28 with total page 550 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far. How can machine learning—especially deep neural networks—make a real difference in your organization? This hands-on guide not only provides the most practical information available on the subject, but also helps you get started building efficient deep learning networks. Authors Adam Gibson and Josh Patterson provide theory on deep learning before introducing their open-source Deeplearning4j (DL4J) library for developing production-class workflows. Through real-world examples, you’ll learn methods and strategies for training deep network architectures and running deep learning workflows on Spark and Hadoop with DL4J. Dive into machine learning concepts in general, as well as deep learning in particular Understand how deep networks evolved from neural network fundamentals Explore the major deep network architectures, including Convolutional and Recurrent Learn how to map specific deep networks to the right problem Walk through the fundamentals of tuning general neural networks and specific deep network architectures Use vectorization techniques for different data types with DataVec, DL4J’s workflow tool Learn how to use DL4J natively on Spark and Hadoop

Download or read book Multi Modal Sentiment Analysis written by Hua Xu and published by Springer Nature. This book was released on 2023-11-26 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: The natural interaction ability between human and machine mainly involves human-machine dialogue ability, multi-modal sentiment analysis ability, human-machine cooperation ability, and so on. To enable intelligent computers to have multi-modal sentiment analysis ability, it is necessary to equip them with a strong multi-modal sentiment analysis ability during the process of human-computer interaction. This is one of the key technologies for efficient and intelligent human-computer interaction. This book focuses on the research and practical applications of multi-modal sentiment analysis for human-computer natural interaction, particularly in the areas of multi-modal information feature representation, feature fusion, and sentiment classification. Multi-modal sentiment analysis for natural interaction is a comprehensive research field that involves the integration of natural language processing, computer vision, machine learning, pattern recognition, algorithm, robot intelligent system, human-computer interaction, etc. Currently, research on multi-modal sentiment analysis in natural interaction is developing rapidly. This book can be used as a professional textbook in the fields of natural interaction, intelligent question answering (customer service), natural language processing, human-computer interaction, etc. It can also serve as an important reference book for the development of systems and products in intelligent robots, natural language processing, human-computer interaction, and related fields.

Download or read book Characterization and Properties of Petroleum Fractions written by M. R. Riazi and published by ASTM International. This book was released on 2005 with total page 425 pages. Available in PDF, EPUB and Kindle. Book excerpt: The last three chapters of this book deal with application of methods presented in previous chapters to estimate various thermodynamic, physical, and transport properties of petroleum fractions. In this chapter, various methods for prediction of physical and thermodynamic properties of pure hydrocarbons and their mixtures, petroleum fractions, crude oils, natural gases, and reservoir fluids are presented. As it was discussed in Chapters 5 and 6, properties of gases may be estimated more accurately than properties of liquids. Theoretical methods of Chapters 5 and 6 for estimation of thermophysical properties generally can be applied to both liquids and gases; however, more accurate properties can be predicted through empirical correlations particularly developed for liquids. When these correlations are developed with some theoretical basis, they are more accurate and have wider range of applications. In this chapter some of these semitheoretical correlations are presented. Methods presented in Chapters 5 and 6 can be used to estimate properties such as density, enthalpy, heat capacity, heat of vaporization, and vapor pressure. Characterization methods of Chapters 2-4 are used to determine the input parameters needed for various predictive methods. One important part of this chapter is prediction of vapor pressure that is needed for vapor-liquid equilibrium calculations of Chapter 9.

Download or read book The Role of Speculation in Oil Markets written by Bassam Fattouh and published by . This book was released on 2012 with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Predicting the Future written by Fernando Sánchez Lasheras and published by MDPI. This book was released on 2020-12-29 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to the increased capabilities of microprocessors and the advent of graphics processing units (GPUs) in recent decades, the use of machine learning methodologies has become popular in many fields of science and technology. This fact, together with the availability of large amounts of information, has meant that machine learning and Big Data have an important presence in the field of Energy. This Special Issue entitled “Predicting the Future—Big Data and Machine Learning” is focused on applications of machine learning methodologies in the field of energy. Topics include but are not limited to the following: big data architectures of power supply systems, energy-saving and efficiency models, environmental effects of energy consumption, prediction of occupational health and safety outcomes in the energy industry, price forecast prediction of raw materials, and energy management of smart buildings.