Download or read book Automated Model Building written by Ricardo Caferra and published by Springer Science & Business Media. This book was released on 2013-11-09 with total page 353 pages. Available in PDF, EPUB and Kindle. Book excerpt: On the history of the book: In the early 1990s several new methods and perspectives in au- mated deduction emerged. We just mention the superposition calculus, meta-term inference and schematization, deductive decision procedures, and automated model building. It was this last ?eld which brought the authors of this book together. In 1994 they met at the Conference on Automated Deduction (CADE-12) in Nancy and agreed upon the general point of view, that semantics and, in particular, construction of models should play a central role in the ?eld of automated deduction. In the following years the deduction groups of the laboratory LEIBNIZ at IMAG Grenoble and the University of Technology in Vienna organized several bilateral projects promoting this topic. This book emerged as a main result of this cooperation. The authors are aware of the fact, that the book does not cover all relevant methods of automated model building (also called model construction or model generation); instead the book focuses on deduction-based symbolic methods for the construction of Herbrand models developed in the last 12 years. Other methods of automated model building, in particular also ?nite model building, are mainly treated in the ?nal chapter; this chapter is less formal and detailed but gives a broader view on the topic and a comparison of di?erent approaches. Howtoreadthisbook: In the introduction we give an overview of automated deduction in a historical context, taking into account its relationship with the human views on formal and informal proofs.

Download or read book Automated Model Building written by Ricardo Caferra and published by . This book was released on 2014-01-15 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Automated Machine Learning written by Frank Hutter and published by Springer. This book was released on 2019-05-17 with total page 223 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.

Download or read book Building Information Modeling written by Nawari O. Nawari and published by CRC Press. This book was released on 2018-02-12 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Many researchers and software developers have put a lot of effort into finding solutions for automated code checking. This book is a good summary of these efforts and provides readers with a comprehensive understanding of the status of such technologies in the industry. It also guides readers on implementation of such techniques using the platforms and tools currently available in the industry." — Issa Ramaji, University of North Florida, USA Building Information Modeling: Automated Code Checking and Compliance Processes covers current and emerging trends in automating the processes of examining building design against codes and standards of practice. The role of Building Information Modeling (BIM) technologies in these processes is thoroughly analyzed and explains how this new technology is significantly transforming modern architecture, engineering, and construction (AEC) domains. The book also introduces the theoretical background of computerizing compliance verification, including domain knowledge representations, building model representations, and automated code checking systems. An underlying goal for the material covered is to present the use of BIM technology as an integral part of the automated auditing process that can lead to a more comprehensive, intelligent, and integrated building design– a design where an optimized solution can be achieved in harmony with the current codes and standards of practice. This new proposed BIM-based framework for automating code conformance checking is one of the most powerful methods presently available to reflect actual building code requirements, and the methods described in the book offer significant benefits to the AEC industry such as:​ Providing consistency in interpretation of regulatory provisions Reducing code compliance validation errors, and the cost and time associated with compliance checking Allows for the ability to self-check required aspects before bidding Reduces the amount of time and resources required during design review Allows for optimal design, along with faster turnaround on feedback, and potentially faster approvals for construction permits by building and infrastructure authorities

Download or read book Machine Learning Automation with TPOT written by Dario Radecic and published by Packt Publishing Ltd. This book was released on 2021-05-07 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover how TPOT can be used to handle automation in machine learning and explore the different types of tasks that TPOT can automate Key FeaturesUnderstand parallelism and how to achieve it in Python.Learn how to use neurons, layers, and activation functions and structure an artificial neural network.Tune TPOT models to ensure optimum performance on previously unseen data.Book Description The automation of machine learning tasks allows developers more time to focus on the usability and reactivity of the software powered by machine learning models. TPOT is a Python automated machine learning tool used for optimizing machine learning pipelines using genetic programming. Automating machine learning with TPOT enables individuals and companies to develop production-ready machine learning models cheaper and faster than with traditional methods. With this practical guide to AutoML, developers working with Python on machine learning tasks will be able to put their knowledge to work and become productive quickly. You'll adopt a hands-on approach to learning the implementation of AutoML and associated methodologies. Complete with step-by-step explanations of essential concepts, practical examples, and self-assessment questions, this book will show you how to build automated classification and regression models and compare their performance to custom-built models. As you advance, you'll also develop state-of-the-art models using only a couple of lines of code and see how those models outperform all of your previous models on the same datasets. By the end of this book, you'll have gained the confidence to implement AutoML techniques in your organization on a production level. What you will learnGet to grips with building automated machine learning modelsBuild classification and regression models with impressive accuracy in a short timeDevelop neural network classifiers with AutoML techniquesCompare AutoML models with traditional, manually developed models on the same datasetsCreate robust, production-ready modelsEvaluate automated classification models based on metrics such as accuracy, recall, precision, and f1-scoreGet hands-on with deployment using Flask-RESTful on localhostWho this book is for Data scientists, data analysts, and software developers who are new to machine learning and want to use it in their applications will find this book useful. This book is also for business users looking to automate business tasks with machine learning. Working knowledge of the Python programming language and beginner-level understanding of machine learning are necessary to get started.

Download or read book Building Machine Learning Pipelines written by Hannes Hapke and published by "O'Reilly Media, Inc.". This book was released on 2020-07-13 with total page 398 pages. Available in PDF, EPUB and Kindle. Book excerpt: Companies are spending billions on machine learning projects, but it’s money wasted if the models can’t be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You’ll learn the techniques and tools that will cut deployment time from days to minutes, so that you can focus on developing new models rather than maintaining legacy systems. Data scientists, machine learning engineers, and DevOps engineers will discover how to go beyond model development to successfully productize their data science projects, while managers will better understand the role they play in helping to accelerate these projects. Understand the steps to build a machine learning pipeline Build your pipeline using components from TensorFlow Extended Orchestrate your machine learning pipeline with Apache Beam, Apache Airflow, and Kubeflow Pipelines Work with data using TensorFlow Data Validation and TensorFlow Transform Analyze a model in detail using TensorFlow Model Analysis Examine fairness and bias in your model performance Deploy models with TensorFlow Serving or TensorFlow Lite for mobile devices Learn privacy-preserving machine learning techniques

Download or read book R for Data Science written by Hadley Wickham and published by "O'Reilly Media, Inc.". This book was released on 2016-12-12 with total page 521 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Automated Machine Learning in Action written by Qingquan Song and published by Simon and Schuster. This book was released on 2022-06-07 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: Automated Machine Learning in Action reveals how you can automate the burdensome elements of designing and tuning your machine learning systems. --

Download or read book Hands On Automated Machine Learning written by Sibanjan Das and published by Packt Publishing Ltd. This book was released on 2018-04-26 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: Automate data and model pipelines for faster machine learning applications Key Features Build automated modules for different machine learning components Understand each component of a machine learning pipeline in depth Learn to use different open source AutoML and feature engineering platforms Book Description AutoML is designed to automate parts of Machine Learning. Readily available AutoML tools are making data science practitioners’ work easy and are received well in the advanced analytics community. Automated Machine Learning covers the necessary foundation needed to create automated machine learning modules and helps you get up to speed with them in the most practical way possible. In this book, you’ll learn how to automate different tasks in the machine learning pipeline such as data preprocessing, feature selection, model training, model optimization, and much more. In addition to this, it demonstrates how you can use the available automation libraries, such as auto-sklearn and MLBox, and create and extend your own custom AutoML components for Machine Learning. By the end of this book, you will have a clearer understanding of the different aspects of automated Machine Learning, and you’ll be able to incorporate automation tasks using practical datasets. You can leverage your learning from this book to implement Machine Learning in your projects and get a step closer to winning various machine learning competitions. What you will learn Understand the fundamentals of Automated Machine Learning systems Explore auto-sklearn and MLBox for AutoML tasks Automate your preprocessing methods along with feature transformation Enhance feature selection and generation using the Python stack Assemble individual components of ML into a complete AutoML framework Demystify hyperparameter tuning to optimize your ML models Dive into Machine Learning concepts such as neural networks and autoencoders Understand the information costs and trade-offs associated with AutoML Who this book is for If you’re a budding data scientist, data analyst, or Machine Learning enthusiast and are new to the concept of automated machine learning, this book is ideal for you. You’ll also find this book useful if you’re an ML engineer or data professional interested in developing quick machine learning pipelines for your projects. Prior exposure to Python programming will help you get the best out of this book.

Download or read book Automated Deduction CADE 18 written by Andrei Voronkov and published by Springer. This book was released on 2003-08-02 with total page 545 pages. Available in PDF, EPUB and Kindle. Book excerpt: The First CADE in the Third Millennium This volume contains the papers presented at the Eighteenth International C- ference on Automated Deduction (CADE-18) held on July 27–30th, 2002, at the University of Copenhagen as part of the Federated Logic Conference (FLoC 2002). Despite a large number of deduction-related conferences springing into existence at the end of the last millennium, the CADE conferences continue to be the major forum for the presentation of new research in all aspects of automated deduction. CADE-18 was sponsored by the Association for Auto- ted Reasoning, CADE Inc., the Department of Computer Science at Chalmers University, the Gesellschaft fur ̈ Informatik, Safelogic AB, and the University of Koblenz-Landau. There were 70 submissions, including 60 regular papers and 10 system - scriptions. Each submission was reviewed by at least ?ve program committee members and an electronic program committee meeting was held via the Int- net. The committee decided to accept 27 regular papers and 9 system descr- tions. One paper switched its category after refereeing, thus the total number of system descriptions in this volume is 10. In addition to the refereed papers, this volume contains an extended abstract of the CADE invited talk by Ian Horrocks, the joint CADE/CAV invited talk by Sharad Malik, and the joint CADE-TABLEAUX invited talk by Matthias Baaz. One more invited lecture was given by Daniel Jackson.

Download or read book Automated Deduction in Classical and Non Classical Logics written by Ricardo Caferra and published by Springer. This book was released on 2003-07-31 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents a collection of thoroughly reviewed revised full papers on automated deduction in classical, modal, and many-valued logics, with an emphasis on first-order theories. Five invited papers by prominent researchers give a consolidated view of the recent developments in first-order theorem proving. The 14 research papers presented went through a twofold selection process and were first presented at the International Workshop on First-Order Theorem Proving, FTP'98, held in Vienna, Austria, in November 1998. The contributed papers reflect the current status in research in the area; most of the results presented rely on resolution or tableaux methods, with a few exceptions choosing the equational paradigm.

Download or read book Practical Automated Machine Learning on Azure written by Deepak Mukunthu and published by "O'Reilly Media, Inc.". This book was released on 2019-09-23 with total page 198 pages. Available in PDF, EPUB and Kindle. Book excerpt: Develop smart applications without spending days and weeks building machine-learning models. With this practical book, you’ll learn how to apply automated machine learning (AutoML), a process that uses machine learning to help people build machine learning models. Deepak Mukunthu, Parashar Shah, and Wee Hyong Tok provide a mix of technical depth, hands-on examples, and case studies that show how customers are solving real-world problems with this technology. Building machine-learning models is an iterative and time-consuming process. Even those who know how to create ML models may be limited in how much they can explore. Once you complete this book, you’ll understand how to apply AutoML to your data right away. Learn how companies in different industries are benefiting from AutoML Get started with AutoML using Azure Explore aspects such as algorithm selection, auto featurization, and hyperparameter tuning Understand how data analysts, BI professions, developers can use AutoML in their familiar tools and experiences Learn how to get started using AutoML for use cases including classification, regression, and forecasting.

Download or read book Handbook of Automated Reasoning written by Alan J.A. Robinson and published by Elsevier. This book was released on 2001-06-21 with total page 1198 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handbook of Automated Reasoning.

Download or read book Logic for Programming and Automated Reasoning written by Michel Parigot and published by Springer. This book was released on 2003-07-31 with total page 499 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 7th International Conference on Logic for Programming and Automated Reasoning, LPAR 2000, held in Reunion Island, France in November 2000. The 26 revised full papers presented together with four invited contributions were carefully reviewed and selected from 65 submissions. The papers are organized in topical sections on nonmonotonic reasoning, descriptive complexity, specification and automatic proof-assistants, theorem proving, verification, logic programming and constraint logic programming, nonclassical logics and the lambda calculus, logic and databases, program analysis, mu-calculus, planning and reasoning about actions.

Download or read book Automating Business Modelling written by Yun-Heh Chen-Burger and published by Springer Science & Business Media. This book was released on 2005-11-27 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: Enhances the use of enterprise models as an effective communication medium between business and technical personnel. Details the blue-print of the to-be developed business system.

Download or read book Automated Reasoning with Analytic Tableaux and Related Methods written by Didier Galmiche and published by Springer Science & Business Media. This book was released on 1997-04-29 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 1998 International Conference on Analytic Tableaux and Related Methods, TABLEAUX'98, held in Oisterwijk near Tilburg, The Netherlands, in May 1998. The volume presents 17 revised full papers and three system descriptions selected from 34 submissions; also included are several abstracts of invited lectures, tutorials, and system comparison papers. The book presents new research results for automated deduction in various non-standard logics as well as in classical logic. Areas of application include software verification, systems verification, deductive databases, knowledge representation and its required inference engines, and system diagnosis.

Download or read book Automated Technology for Verification and Analysis written by Ahmed Bouajjani and published by Springer Science & Business Media. This book was released on 2010-09-03 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: place.