Machine learning for automated theorem proving, Holden, Sean B.
Автор: Pillay Nelishia, Qu Rong Название: Automated Design of Machine Learning and Search Algorithms ISBN: 3030720683 ISBN-13(EAN): 9783030720681 Издательство: Springer Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Also covered are automated generation of constructive and perturbative low-level heuristics, selection hyper-heuristics for automated design, automated design of deep-learning approaches using hyper-heuristics, genetic programming hyper-heuristics with transfer knowledge and automated design of classification algorithms.
Автор: Zhu Wenwu, Wang Xin Название: Automated Machine Learning and Meta-Learning for Multimedia ISBN: 3030881318 ISBN-13(EAN): 9783030881313 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book disseminates and promotes the recent research progress and frontier development on AutoML and meta-learning as well as their applications on computer vision, natural language processing, multimedia and data mining related fields.
Автор: Das Sibanjan, Cakmak Umit Mert Название: Hands-On Automated Machine Learning ISBN: 1788629892 ISBN-13(EAN): 9781788629898 Издательство: Неизвестно Рейтинг: Цена: 6544.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book helps machine learning professionals in developing AutoML systems that can be utilized to build ML solutions. This book covers the necessary foundations and shows the most practical ways possible to get to speed with regards to creating AutoML modules.
Автор: Fatima Название: Principles of Automated Negotiation ISBN: 1107002540 ISBN-13(EAN): 9781107002548 Издательство: Cambridge Academ Рейтинг: Цена: 7602.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: With an increasing number of applications in the context of multi-agent systems, automated negotiation is a rapidly growing area. Written by top researchers in the field, this state-of-the-art treatment of the subject explores key issues involved in the design of negotiating agents, covering strategic, heuristic, and axiomatic approaches. The authors discuss the potential benefits of automated negotiation as well as the unique challenges it poses for computer scientists and for researchers in artificial intelligence. They also consider possible applications and give readers a feel for the types of domains where automated negotiation is already being deployed. This book is ideal for graduate students and researchers in computer science who are interested in multi-agent systems. It will also appeal to negotiation researchers from disciplines such as management and business studies, psychology and economics.
Описание: This book describes efforts to improve subject-independent automated classification techniques using a better feature extraction method and a more efficient model of classification. It evaluates three popular saliency criteria for feature selection, showing that they share common limitations, including time-consuming and subjective manual de-facto standard practice, and that existing automated efforts have been predominantly used for subject dependent setting. It then proposes a novel approach for anomaly detection, demonstrating its effectiveness and accuracy for automated classification of biomedical data, and arguing its applicability to a wider range of unsupervised machine learning applications in subject-independent settings.
Описание: This open access book offers a comprehensive and thorough introduction to almost all aspects of metalearning and automated machine learning (AutoML), covering the basic concepts and architecture, evaluation, datasets, hyperparameter optimization, ensembles and workflows, and also how this knowledge can be used to select, combine, compose, adapt and configure both algorithms and models to yield faster and better solutions to data mining and data science problems. It can thus help developers to develop systems that can improve themselves through experience. As one of the fastest-growing areas of research in machine learning, metalearning studies principled methods to obtain efficient models and solutions by adapting machine learning and data mining processes. This adaptation usually exploits information from past experience on other tasks and the adaptive processes can involve machine learning approaches. As a related area to metalearning and a hot topic currently, AutoML is concerned with automating the machine learning processes. Metalearning and AutoML can help AI learn to control the application of different learning methods and acquire new solutions faster without unnecessary interventions from the user. This book is a substantial update of the first edition published in 2009. It includes 18 chapters, more than twice as much as the previous version. This enabled the authors to cover the most relevant topics in more depth and incorporate the overview of recent research in the respective area. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining, data science and artificial intelligence.
Автор: Pardalos Panos M., Rasskazova Varvara, Vrahatis Michael N. Название: Black Box Optimization, Machine Learning, and No-Free Lunch Theorems ISBN: 3030665143 ISBN-13(EAN): 9783030665142 Издательство: Springer Цена: 18167.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The book is intended for beginners who wish to achieve a broad overview of optimization methods and also for more experienced researchers as well as researchers in mathematics, optimization, operations research, quantitative logistics, data analysis, and statistics, who will benefit from access to a quick reference to key topics and methods.
A practical, step-by-step guide to using Microsoft's AutoML technology on the Azure Machine Learning service for developers and data scientists working with the Python programming language
Key Features:
Create, deploy, productionalize, and scale automated machine learning solutions on Microsoft Azure
Improve the accuracy of your ML models through automatic data featurization and model training
Increase productivity in your organization by using artificial intelligence to solve common problems
Book Description:
Automated Machine Learning with Microsoft Azure helps you to build high-performing, accurate machine learning models in record time. It allows anyone to easily harness the power of artificial intelligence and increase the productivity and profitability of your business. With a series of clicks on a guided user interface (GUI), novices and seasoned data scientists alike can train and deploy machine learning solutions to production with ease.
This book will teach you how to use Azure AutoML with both the GUI as well as the AzureML Python software development kit (SDK) in a careful, step-by-step way. First, you'll learn how to prepare data, train models, and register them to your Azure Machine Learning workspace. You'll then discover how to take those models and use them to create both automated batch solutions using machine learning pipelines and real-time scoring solutions using Azure Kubernetes Service (AKS). Finally, you will be able to use AutoML on your own data to not only train regression, classification, and forecasting models but also use them to solve a wide variety of business problems.
By the end of this Azure book, you'll be able to show your business partners exactly how your ML models are making predictions through automatically generated charts and graphs, earning their trust and respect.
What You Will Learn:
Understand how to train classification, regression, and forecasting ML algorithms with Azure AutoML
Prepare data for Azure AutoML to ensure smooth model training and deployment
Adjust AutoML configuration settings to make your models as accurate as possible
Determine when to use a batch-scoring solution versus a real-time scoring solution
Productionalize your AutoML solution with Azure Machine Learning pipelines
Create real-time scoring solutions with AutoML and Azure Kubernetes Service
Discover how to quickly deliver value and earn business trust using AutoML
Train a large number of AutoML models at once using the AzureML Python SDK
Who this book is for:
Data scientists, aspiring data scientists, machine learning engineers, or anyone interested in applying artificial intelligence or machine learning in their business will find this book useful. You need to have beginner-level knowledge of artificial intelligence and a technical background in computer science, statistics, or information technology before getting started with this machine learning book. Familiarity with Python will help you implement this book's more advanced features, but even data analysts and SQL experts will be able to train ML models after finishing this book.
Описание: This book offers a clear understanding of the concept of context-aware machine learning including an automated rule-based framework within the broad area of data science and analytics, particularly, with the aim of data-driven intelligent decision making.
Create better and easy-to-use deep learning models with AutoKeras
Key Features:
Design and implement your own custom machine learning models using the features of AutoKeras
Learn how to use AutoKeras for techniques such as classification, regression, and sentiment analysis
Get familiar with advanced concepts as multi-modal, multi-task, and search space customization
Book Description
AutoKeras is an AutoML open-source software library that provides easy access to deep learning models. If you are looking to build deep learning model architectures and perform parameter tuning automatically using AutoKeras, then this book is for you.
This book teaches you how to develop and use state-of-the-art AI algorithms in your projects. It begins with a high-level introduction to automated machine learning, explaining all the concepts required to get started with this machine learning approach. You will then learn how to use AutoKeras for image and text classification and regression. As you make progress, you'll discover how to use AutoKeras to perform sentiment analysis on documents. This book will also show you how to implement a custom model for topic classification with AutoKeras. Toward the end, you will explore advanced concepts of AutoKeras such as working with multi-modal data and multi-task, customizing the model with AutoModel, and visualizing experiment results using AutoKeras Extensions.
By the end of this machine learning book, you will be able to confidently use AutoKeras to design your own custom machine learning models in your company.
What You Will Learn:
Set up a deep learning workstation with TensorFlow and AutoKeras
Automate a machine learning pipeline with AutoKeras
Create and implement image and text classifiers and regressors using AutoKeras
Use AutoKeras to perform sentiment analysis of a text, classifying it as negative or positive
Leverage AutoKeras to classify documents by topics
Make the most of AutoKeras by using its most powerful extensions
Who this book is for:
This book is for machine learning and deep learning enthusiasts who want to apply automated ML techniques to their projects. Prior basic knowledge of Python programming and machine learning is expected to get the most out of this book.
Get to grips with automated machine learning and adopt a hands-on approach to AutoML implementation and associated methodologies
Key Features:
Get up to speed with AutoML using OSS, Azure, AWS, GCP, or any platform of your choice
Eliminate mundane tasks in data engineering and reduce human errors in machine learning models
Find out how you can make machine learning accessible for all users to promote decentralized processes
Book Description:
Every machine learning engineer deals with systems that have hyperparameters, and the most basic task in automated machine learning (AutoML) is to automatically set these hyperparameters to optimize performance. The latest deep neural networks have a wide range of hyperparameters for their architecture, regularization, and optimization, which can be customized effectively to save time and effort.
This book reviews the underlying techniques of automated feature engineering, model and hyperparameter tuning, gradient-based approaches, and much more. You'll discover different ways of implementing these techniques in open source tools and then learn to use enterprise tools for implementing AutoML in three major cloud service providers: Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform. As you progress, you'll explore the features of cloud AutoML platforms by building machine learning models using AutoML. The book will also show you how to develop accurate models by automating time-consuming and repetitive tasks in the machine learning development lifecycle.
By the end of this machine learning book, you'll be able to build and deploy AutoML models that are not only accurate, but also increase productivity, allow interoperability, and minimize feature engineering tasks.
What You Will Learn:
Explore AutoML fundamentals, underlying methods, and techniques
Assess AutoML aspects such as algorithm selection, auto featurization, and hyperparameter tuning in an applied scenario
Find out the difference between cloud and operations support systems (OSS)
Implement AutoML in enterprise cloud to deploy ML models and pipelines
Build explainable AutoML pipelines with transparency
Understand automated feature engineering and time series forecasting
Automate data science modeling tasks to implement ML solutions easily and focus on more complex problems
Who this book is for:
Citizen data scientists, machine learning developers, artificial intelligence enthusiasts, or anyone looking to automatically build machine learning models using the features offered by open source tools, Microsoft Azure Machine Learning, AWS, and Google Cloud Platform will find this book useful. Beginner-level knowledge of building ML models is required to get the best out of this book. Prior experience in using Enterprise cloud is beneficial.
Название: Automated machine learning ISBN: 3030053172 ISBN-13(EAN): 9783030053178 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 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.
ООО "Логосфера " Тел:+7(495) 980-12-10 www.logobook.ru
