Distributed Resource Location Using Approximate Inference

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Distributed Resource Location Using Approximate Inference

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Author : Keith Bradley Vanderveen
language : en
Publisher:
Release Date : 1998

Distributed Resource Location Using Approximate Inference written by Keith Bradley Vanderveen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998 with categories.

Variational Methods For Machine Learning With Applications To Deep Networks

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Author : Lucas Pinheiro Cinelli
language : en
Publisher: Springer Nature
Release Date : 2021-05-10

Variational Methods For Machine Learning With Applications To Deep Networks written by Lucas Pinheiro Cinelli and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-10 with Technology & Engineering categories.

This book provides a straightforward look at the concepts, algorithms and advantages of Bayesian Deep Learning and Deep Generative Models. Starting from the model-based approach to Machine Learning, the authors motivate Probabilistic Graphical Models and show how Bayesian inference naturally lends itself to this framework. The authors present detailed explanations of the main modern algorithms on variational approximations for Bayesian inference in neural networks. Each algorithm of this selected set develops a distinct aspect of the theory. The book builds from the ground-up well-known deep generative models, such as Variational Autoencoder and subsequent theoretical developments. By also exposing the main issues of the algorithms together with different methods to mitigate such issues, the book supplies the necessary knowledge on generative models for the reader to handle a wide range of data types: sequential or not, continuous or not, labelled or not. The book is self-contained, promptly covering all necessary theory so that the reader does not have to search for additional information elsewhere. Offers a concise self-contained resource, covering the basic concepts to the algorithms for Bayesian Deep Learning; Presents Statistical Inference concepts, offering a set of elucidative examples, practical aspects, and pseudo-codes; Every chapter includes hands-on examples and exercises and a website features lecture slides, additional examples, and other support material.

Dissertation Abstracts International

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Author :
language : en
Publisher:
Release Date : 2007

Dissertation Abstracts International written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Dissertations, Academic categories.

Machine Learning For Algorithmic Trading

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Author : Stefan Jansen
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-07-31

Machine Learning For Algorithmic Trading written by Stefan Jansen and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-31 with Business & Economics categories.

Leverage machine learning to design and back-test automated trading strategies for real-world markets using pandas, TA-Lib, scikit-learn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and pyfolio. Purchase of the print or Kindle book includes a free eBook in the PDF format. Key Features Design, train, and evaluate machine learning algorithms that underpin automated trading strategies Create a research and strategy development process to apply predictive modeling to trading decisions Leverage NLP and deep learning to extract tradeable signals from market and alternative data Book DescriptionThe explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models. This book introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting. It illustrates this by using examples ranging from linear models and tree-based ensembles to deep-learning techniques from cutting edge research. This edition shows how to work with market, fundamental, and alternative data, such as tick data, minute and daily bars, SEC filings, earnings call transcripts, financial news, or satellite images to generate tradeable signals. It illustrates how to engineer financial features or alpha factors that enable an ML model to predict returns from price data for US and international stocks and ETFs. It also shows how to assess the signal content of new features using Alphalens and SHAP values and includes a new appendix with over one hundred alpha factor examples. By the end, you will be proficient in translating ML model predictions into a trading strategy that operates at daily or intraday horizons, and in evaluating its performance.What you will learn Leverage market, fundamental, and alternative text and image data Research and evaluate alpha factors using statistics, Alphalens, and SHAP values Implement machine learning techniques to solve investment and trading problems Backtest and evaluate trading strategies based on machine learning using Zipline and Backtrader Optimize portfolio risk and performance analysis using pandas, NumPy, and pyfolio Create a pairs trading strategy based on cointegration for US equities and ETFs Train a gradient boosting model to predict intraday returns using AlgoSeek s high-quality trades and quotes data Who this book is for If you are a data analyst, data scientist, Python developer, investment analyst, or portfolio manager interested in getting hands-on machine learning knowledge for trading, this book is for you. This book is for you if you want to learn how to extract value from a diverse set of data sources using machine learning to design your own systematic trading strategies. Some understanding of Python and machine learning techniques is required.

Statistical Methods Computing And Resources For Genome Wide Association Studies

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Author : Riyan Cheng
language : en
Publisher: Frontiers Media SA
Release Date : 2021-08-24

Statistical Methods Computing And Resources For Genome Wide Association Studies written by Riyan Cheng and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-24 with Science categories.

American Doctoral Dissertations

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Author :
language : en
Publisher:
Release Date : 1998

American Doctoral Dissertations written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998 with Dissertation abstracts categories.

Statistics In Natural Resources

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Author : Matthew Russell
language : en
Publisher: CRC Press
Release Date : 2022-08-19

Statistics In Natural Resources written by Matthew Russell and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-19 with Technology & Engineering categories.

To manage our environment sustainably, professionals must understand the quality and quantity of our natural resources. Statistical analysis provides information that supports management decisions and is universally used across scientific disciplines. Statistics in Natural Resources: Applications with R focuses on the application of statistical analyses in the environmental, agricultural, and natural resources disciplines. This is a book well suited for current or aspiring natural resource professionals who are required to analyze data and perform statistical analyses in their daily work. More seasoned professionals who have previously had a course or two in statistics will also find the content familiar. This text can also serve as a bridge between professionals who understand statistics and want to learn how to perform analyses on natural resources data in R. The primary goal of this book is to learn and apply common statistical methods used in natural resources by using the R programming language. If you dedicate considerable time to this book, you will: Develop analytical and visualization skills for investigating the behavior of agricultural and natural resources data. Become competent in importing, analyzing, and visualizing complex data sets in the R environment. Recode, combine, and restructure data sets for statistical analysis and visualization. Appreciate probability concepts as they apply to environmental problems. Understand common distributions used in statistical applications and inference. Summarize data effectively and efficiently for reporting purposes. Learn the tasks required to perform a variety of statistical hypothesis tests and interpret their results. Understand which modeling frameworks are appropriate for your data and how to interpret predictions. Includes over 130 exercises in R, with solutions available on the book’s website.

Big Data Analytics In Hiv Aids Research

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Author : Al Mazari, Ali
language : en
Publisher: IGI Global
Release Date : 2018-04-27

Big Data Analytics In Hiv Aids Research written by Al Mazari, Ali and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-04-27 with Medical categories.

With the advent of new technologies in big data science, the study of medical problems has made significant progress. Connecting medical studies and computational methods is crucial for the advancement of the medical industry. Big Data Analytics in HIV/AIDS Research provides emerging research on the development and implementation of computational techniques in big data analysis for biological and medical practices. While highlighting topics such as deep learning, management software, and molecular modeling, this publication explores the various applications of data analysis in clinical decision making. This book is a vital resource for medical practitioners, nurses, scientists, researchers, and students seeking current research on the connections between data analytics in the field of medicine.

Big Data Data Mining And Data Science

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Author : George Dimitoglou
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2024-12-30

Big Data Data Mining And Data Science written by George Dimitoglou and has been published by Walter de Gruyter GmbH & Co KG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-30 with Computers categories.

Through the application of cutting-edge techniques like Big Data, Data Mining, and Data Science, it is possible to extract insights from massive datasets. These methodologies are crucial in enabling informed decision-making and driving transformative advancements across many fields, industries, and domains. This book offers an overview of latest tools, methods and approaches while also highlighting their practical use through various applications and case studies.

Analysis Of Generalized Linear Mixed Models In The Agricultural And Natural Resources Sciences

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Author : Edward E. Gbur
language : en
Publisher: John Wiley & Sons
Release Date : 2020-01-22

Analysis Of Generalized Linear Mixed Models In The Agricultural And Natural Resources Sciences written by Edward E. Gbur and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-22 with Technology & Engineering categories.

Generalized Linear Mixed Models in the Agricultural and Natural Resources Sciences provides readers with an understanding and appreciation for the design and analysis of mixed models for non-normally distributed data. It is the only publication of its kind directed specifically toward the agricultural and natural resources sciences audience. Readers will especially benefit from the numerous worked examples based on actual experimental data and the discussion of pitfalls associated with incorrect analyses.