Introduction To Statistical Relational Learning
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Introduction To Statistical Relational Learning
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Author : Lise Getoor
language : en
Publisher: MIT Press
Release Date : 2019-09-22
Introduction To Statistical Relational Learning written by Lise Getoor and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-22 with Computers categories.
Advanced statistical modeling and knowledge representation techniques for a newly emerging area of machine learning and probabilistic reasoning; includes introductory material, tutorials for different proposed approaches, and applications. Handling inherent uncertainty and exploiting compositional structure are fundamental to understanding and designing large-scale systems. Statistical relational learning builds on ideas from probability theory and statistics to address uncertainty while incorporating tools from logic, databases and programming languages to represent structure. In Introduction to Statistical Relational Learning, leading researchers in this emerging area of machine learning describe current formalisms, models, and algorithms that enable effective and robust reasoning about richly structured systems and data. The early chapters provide tutorials for material used in later chapters, offering introductions to representation, inference and learning in graphical models, and logic. The book then describes object-oriented approaches, including probabilistic relational models, relational Markov networks, and probabilistic entity-relationship models as well as logic-based formalisms including Bayesian logic programs, Markov logic, and stochastic logic programs. Later chapters discuss such topics as probabilistic models with unknown objects, relational dependency networks, reinforcement learning in relational domains, and information extraction. By presenting a variety of approaches, the book highlights commonalities and clarifies important differences among proposed approaches and, along the way, identifies important representational and algorithmic issues. Numerous applications are provided throughout.
Logical And Relational Learning
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Author : Luc De Raedt
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-09-27
Logical And Relational Learning written by Luc De Raedt and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-09-27 with Computers categories.
This first textbook on multi-relational data mining and inductive logic programming provides a complete overview of the field. It is self-contained and easily accessible for graduate students and practitioners of data mining and machine learning.
Relational Data Mining
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Author : Saso Dzeroski
language : en
Publisher: Springer Science & Business Media
Release Date : 2001-08
Relational Data Mining written by Saso Dzeroski and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-08 with Business & Economics categories.
As the first book devoted to relational data mining, this coherently written multi-author monograph provides a thorough introduction and systematic overview of the area. The first part introduces the reader to the basics and principles of classical knowledge discovery in databases and inductive logic programming; subsequent chapters by leading experts assess the techniques in relational data mining in a principled and comprehensive way; finally, three chapters deal with advanced applications in various fields and refer the reader to resources for relational data mining. This book will become a valuable source of reference for R&D professionals active in relational data mining. Students as well as IT professionals and ambitioned practitioners interested in learning about relational data mining will appreciate the book as a useful text and gentle introduction to this exciting new field.
R For Data Science
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Author : Hadley Wickham
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2016-12-12
R For Data Science written by Hadley Wickham and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-12-12 with Computers categories.
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results
Probabilistic Deep Learning
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Author : Oliver Duerr
language : en
Publisher: Manning
Release Date : 2020-11-10
Probabilistic Deep Learning written by Oliver Duerr and has been published by Manning this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-10 with Computers categories.
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
Introduction To Statistical And Machine Learning Methods For Data Science
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Author : Carlos Andre Reis Pinheiro
language : en
Publisher: SAS Institute
Release Date : 2021-08-06
Introduction To Statistical And Machine Learning Methods For Data Science written by Carlos Andre Reis Pinheiro and has been published by SAS Institute this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-06 with Computers categories.
Boost your understanding of data science techniques to solve real-world problems Data science is an exciting, interdisciplinary field that extracts insights from data to solve business problems. This book introduces common data science techniques and methods and shows you how to apply them in real-world case studies. From data preparation and exploration to model assessment and deployment, this book describes every stage of the analytics life cycle, including a comprehensive overview of unsupervised and supervised machine learning techniques. The book guides you through the necessary steps to pick the best techniques and models and then implement those models to successfully address the original business need. No software is shown in the book, and mathematical details are kept to a minimum. This allows you to develop an understanding of the fundamentals of data science, no matter what background or experience level you have.
Collaborative Filtering Using Data Mining And Analysis
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Author : Bhatnagar, Vishal
language : en
Publisher: IGI Global
Release Date : 2016-07-13
Collaborative Filtering Using Data Mining And Analysis written by Bhatnagar, Vishal and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-07-13 with Computers categories.
Internet usage has become a normal and essential aspect of everyday life. Due to the immense amount of information available on the web, it has become obligatory to find ways to sift through and categorize the overload of data while removing redundant material. Collaborative Filtering Using Data Mining and Analysis evaluates the latest patterns and trending topics in the utilization of data mining tools and filtering practices. Featuring emergent research and optimization techniques in the areas of opinion mining, text mining, and sentiment analysis, as well as their various applications, this book is an essential reference source for researchers and engineers interested in collaborative filtering.
Natural Language Processing Concepts Methodologies Tools And Applications
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Author : Management Association, Information Resources
language : en
Publisher: IGI Global
Release Date : 2019-11-01
Natural Language Processing Concepts Methodologies Tools And Applications written by Management Association, Information Resources and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-01 with Computers categories.
As technology continues to become more sophisticated, a computer’s ability to understand, interpret, and manipulate natural language is also accelerating. Persistent research in the field of natural language processing enables an understanding of the world around us, in addition to opportunities for manmade computing to mirror natural language processes that have existed for centuries. Natural Language Processing: Concepts, Methodologies, Tools, and Applications is a vital reference source on the latest concepts, processes, and techniques for communication between computers and humans. Highlighting a range of topics such as machine learning, computational linguistics, and semantic analysis, this multi-volume book is ideally designed for computer engineers, computer and software developers, IT professionals, academicians, researchers, and upper-level students seeking current research on the latest trends in the field of natural language processing.
Statistics For Machine Learning
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Author : Pratap Dangeti
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-07-21
Statistics For Machine Learning written by Pratap Dangeti 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 2017-07-21 with Computers categories.
Build Machine Learning models with a sound statistical understanding. About This Book Learn about the statistics behind powerful predictive models with p-value, ANOVA, and F- statistics. Implement statistical computations programmatically for supervised and unsupervised learning through K-means clustering. Master the statistical aspect of Machine Learning with the help of this example-rich guide to R and Python. Who This Book Is For This book is intended for developers with little to no background in statistics, who want to implement Machine Learning in their systems. Some programming knowledge in R or Python will be useful. What You Will Learn Understand the Statistical and Machine Learning fundamentals necessary to build models Understand the major differences and parallels between the statistical way and the Machine Learning way to solve problems Learn how to prepare data and feed models by using the appropriate Machine Learning algorithms from the more-than-adequate R and Python packages Analyze the results and tune the model appropriately to your own predictive goals Understand the concepts of required statistics for Machine Learning Introduce yourself to necessary fundamentals required for building supervised & unsupervised deep learning models Learn reinforcement learning and its application in the field of artificial intelligence domain In Detail Complex statistics in Machine Learning worry a lot of developers. Knowing statistics helps you build strong Machine Learning models that are optimized for a given problem statement. This book will teach you all it takes to perform complex statistical computations required for Machine Learning. You will gain information on statistics behind supervised learning, unsupervised learning, reinforcement learning, and more. Understand the real-world examples that discuss the statistical side of Machine Learning and familiarize yourself with it. You will also design programs for performing tasks such as model, parameter fitting, regression, classification, density collection, and more. By the end of the book, you will have mastered the required statistics for Machine Learning and will be able to apply your new skills to any sort of industry problem. Style and approach This practical, step-by-step guide will give you an understanding of the Statistical and Machine Learning fundamentals you'll need to build models.
Advances In Artificial Intelligence Sbia 2008
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Author : Gerson Zaverucha
language : en
Publisher: Springer
Release Date : 2008-10-17
Advances In Artificial Intelligence Sbia 2008 written by Gerson Zaverucha and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-10-17 with Computers categories.
This book constitutes the refereed proceedings of the 19th Brazilian Symposium on Artificial Intelligence, SBIA 2008, held in Salvador, Brazil, in October 2008. The 27 revised full papers presented together with 3 invited lectures and 3 tutorials were carefully reviewed and selected from 142 submissions. The papers are organized in topical sections on computer vision and pattern recognition, distributed AI: autonomous agents, multi-agent systems and game knowledge representation and reasoning, machine learning and data mining, natural language processing, and robotics.