Welcome to our book review site go-pdf.online!
You may have to Search all our reviewed books and magazines, click the sign up button below to create a free account.
Predictive Analytics with Microsoft Azure Machine Learning
Data Science and Machine Learning are in high demand, as customers are increasingly looking for ways to glean insights from all their data. More customers now realize that Business Intelligence is not enough as the volume, speed and complexity of data now defy traditional analytics tools. While Business Intelligence addresses descriptive and diagnostic analysis, Data Science unlocks new opportunities through predictive and prescriptive analysis. The purpose of this book is to provide a gentle and instructionally organized introduction to the field of data science and machine learning, with a focus on building and deploying predictive models. The book also provides a thorough overview of the ...
Practical Automated Machine Learning on Azure
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.
Practical Weak Supervision
Most data scientists and engineers today rely on quality labeled data to train machine learning models. But building a training set manually is time-consuming and expensive, leaving many companies with unfinished ML projects. There's a more practical approach. In this book, Wee Hyong Tok, Amit Bahree, and Senja Filipi show you how to create products using weakly supervised learning models. You'll learn how to build natural language processing and computer vision projects using weakly labeled datasets from Snorkel, a spin-off from the Stanford AI Lab. Because so many companies have pursued ML projects that never go beyond their labs, this book also provides a guide on how to ship the deep learning models you build. Get up to speed on the field of weak supervision, including ways to use it as part of the data science process Use Snorkel AI for weak supervision and data programming Get code examples for using Snorkel to label text and image datasets Use a weakly labeled dataset for text and image classification Learn practical considerations for using Snorkel with large datasets and using Spark clusters to scale labeling
Microsoft SQL Server 2012 Integration Services
Build and manage data integration solutions with expert guidance from the Microsoft SQL Server Integration Services (SSIS) team. See best practices in action and dive deep into the SSIS engine, SSISDB catalog, and security features. Using the developer enhancements in SQL Server 2012 and the flexible SSIS toolset, you’ll handle complex data integration scenarios more efficiently—and acquire the skills you need to build comprehensive solutions. Discover how to: Use SSIS to extract, transform, and load data from multiple data sources Apply best practices to optimize package and project configuration and deployment Manage security settings in the SSISDB catalog and control package access Work with SSIS data quality features to profile, cleanse, and increase reliability Monitor, troubleshoot, and tune SSIS solutions with advanced features such as detailed views and data taps Load data incrementally to capture an easily consumable stream of insert, update, and delete activity
Predictive Analytics with Microsoft Azure Machine Learning 2nd Edition
Predictive Analytics with Microsoft Azure Machine Learning, Second Edition is a practical tutorial introduction to the field of data science and machine learning, with a focus on building and deploying predictive models. The book provides a thorough overview of the Microsoft Azure Machine Learning service released for general availability on February 18th, 2015 with practical guidance for building recommenders, propensity models, and churn and predictive maintenance models. The authors use task oriented descriptions and concrete end-to-end examples to ensure that the reader can immediately begin using this new service. The book describes all aspects of the service from data ingress to applyi...
Generative AI in Action
Generative AI can transform your business by streamlining the process of creating text, images, and code. This book will show you how to get in on the action! Generative AI in Action is the comprehensive and concrete guide to generative AI you’ve been searching for. It introduces both AI’s fundamental principles and its practical applications in an enterprise context—from generating text and images for product catalogs and marketing campaigns, to technical reporting, and even writing software. Inside, author Amit Bahree shares his experience leading Generative AI projects at Microsoft for nearly a decade, starting well before the current GPT revolution. Inside Generative AI in Action y...
Handbook of Research on Mobile Multimedia, Second Edition
"The book is intended to clarify the hype, which surrounds the concept of mobile multimedia through introducing the idea in a clear and understandable way, with a strong focus on mobile solutions and applications"--Provided by publisher.
Advances in Database Technology - EDBT 2002
The Eighth International Conference on Extending Database Technology, EDBT 2002, was held in Prague, Czech Republic, March 25–27, 2002. It marks the 50th anniversary of Charles University’s Faculty of Mathematics and Physics and is the most recent in a series of conferences dedicated to the dissemination and exchange of the latest advances in data management. Previous conferences occurred in Konstanz, Valencia, Avignon, Cambridge, Vienna, and Venice. The topical theme of this year’s conference is Data Management in the New Millennium, which encourages the community to see beyond the management of massive databases by conventional database management systems and to extend database techn...
Database and Expert Systems Applications
This volume constitutes the refereed proceedings of the 18th International Conference on Database and Expert Systems Applications held in September 2007. Papers are organized into topical sections covering XML, data and information, datamining and data warehouses, database applications, WWW, bioinformatics, process automation and workflow, knowledge management and expert systems, database theory, query processing, and privacy and security.
Database Systems for Advanced Applications
Coverage in this proceedings includes XML schemas, data mining, spatial data, indexes and cubes, data streams, P2P and transactions, complex pattern processing, IR techniques, queries and transactions, XML databases, data warehouses, and distributed data.
