Author: Publisher:
ISBN:
Category : Meteorology
Languages : en
Pages : 36
Book Description
Preliminary Data Summary
Preliminary Data Summary of the Metal Finishing Industry
Author: Preliminary data summary airport deicing operations.
Author: Publisher: DIANE Publishing
ISBN: 1428905790
Category : Airplanes
Languages : en
Pages : 447
Book Description
Introduction to Multivariate Analysis
Author: Chris ChatfieldPublisher: CRC Press
ISBN: 9780412160400
Category : Mathematics
Languages : en
Pages : 262
Book Description
This book provides an introduction to the analysis of multivariate data.It describes multivariate probability distributions, the preliminary analysisof a large -scale set of data, princ iple component and factor analysis, traditional normal theory material, as well as multidimensional scaling andcluster analysis.Introduction to Multivariate Analysis provides a reasonable blend oftheory and practice. Enough theory is given to introduce the concepts andto make the topics mathematically interesting. In addition the authors discussthe use (and misuse) of the techniques in pra ctice and present appropriatereal-life examples from a variety of areas includ ing agricultural research, soc iology and crim inology. The book should be suitable both for researchworkers and as a text for students taking a course on multivariate analysi
Preliminary Data Analysis; 1968 National Survey of Community Solid Waste Practices
Author: Anton J. MuhichPublisher:
ISBN:
Category : Refuse and refuse disposal
Languages : en
Pages : 512
Book Description
EPA National Publications Catalog
Author: United States. Environmental Protection AgencyPublisher:
ISBN:
Category : Environmental protection
Languages : en
Pages : 896
Book Description
Energy Research Abstracts
Author: Statistical Bulletin
Author: Federal Register
Author: Comet for Data Science
Author: Angelica Lo DucaPublisher: Packt Publishing Ltd
ISBN: 180181435X
Category : Computers
Languages : en
Pages : 402
Book Description
Gain the key knowledge and skills required to manage data science projects using Comet Key Features • Discover techniques to build, monitor, and optimize your data science projects • Move from prototyping to production using Comet and DevOps tools • Get to grips with the Comet experimentation platform Book Description This book provides concepts and practical use cases which can be used to quickly build, monitor, and optimize data science projects. Using Comet, you will learn how to manage almost every step of the data science process from data collection through to creating, deploying, and monitoring a machine learning model. The book starts by explaining the features of Comet, along with exploratory data analysis and model evaluation in Comet. You'll see how Comet gives you the freedom to choose from a selection of programming languages, depending on which is best suited to your needs. Next, you will focus on workspaces, projects, experiments, and models. You will also learn how to build a narrative from your data, using the features provided by Comet. Later, you will review the basic concepts behind DevOps and how to extend the GitLab DevOps platform with Comet, further enhancing your ability to deploy your data science projects. Finally, you will cover various use cases of Comet in machine learning, NLP, deep learning, and time series analysis, gaining hands-on experience with some of the most interesting and valuable data science techniques available. By the end of this book, you will be able to confidently build data science pipelines according to bespoke specifications and manage them through Comet. What you will learn • Prepare for your project with the right data • Understand the purposes of different machine learning algorithms • Get up and running with Comet to manage and monitor your pipelines • Understand how Comet works and how to get the most out of it • See how you can use Comet for machine learning • Discover how to integrate Comet with GitLab • Work with Comet for NLP, deep learning, and time series analysis Who this book is for This book is for anyone who has programming experience, and wants to learn how to manage and optimize a complete data science lifecycle using Comet and other DevOps platforms. Although an understanding of basic data science concepts and programming concepts is needed, no prior knowledge of Comet and DevOps is required.