Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Comet for Data Science PDF full book. Access full book title Comet for Data Science by Angelica Lo Duca. Download full books in PDF and EPUB format.
Author: Angelica Lo Duca Publisher: 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.
Author: Angelica Lo Duca Publisher: 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.
Author: United States. Congress. House. Committee on Appropriations. Subcommittee on Commerce, Justice, Science, and Related Agencies Publisher: ISBN: Category : United States Languages : en Pages : 1190
Author: Michael Grewing Publisher: Springer Science & Business Media ISBN: 3642829716 Category : Science Languages : en Pages : 1005
Book Description
The 1985/86 apparition of Halley's Comet turned out to be the most important apparition of a comet ever. It provided a worldwide science community with a wealth of exciting new discoveries, the most remarkable of which was undoubtedly the first image of a cometary nucleus. Halley's Comet is the brightest periodic comet, and the most famous of the 750 known comets. With its 76-year period, its recent appearance was truly a "once-in-a-lifetime" observational opportunity. The 1985/86 apparition was the thirtieth consecutive recorded apparition. Five apparitions ago, the English astronomer Edmond Halley discovered the periodicity of "his" comet and correctly predicted its return in 1758, a triumph for science best appreciated in the context of contemporary views, or rather fears, about comets at that time. The increasingly rapid progress in technological development is very much apparent when one compares the dominant tools for cometary research during Halley's next three apparitions: in 1835 studies were made based on drawings ofthe comet; in 1910 photographic plates were used; while in March 1986 an armada of six spacecraft from four space agencies approached the comet and carried out in situ measurements, 1 AU from the Earth. In 1910, nobody could have dreamed that this was possible, and today it is equally difficult to anticipate what scientists will be able to achieve in 2061.
Author: United States. Congress. House. Committee on Appropriations. Subcommittee on Commerce, Justice, Science, and Related Agencies Publisher: ISBN: Category : Administrative agencies Languages : en Pages : 1266
Author: John D. Kelleher Publisher: MIT Press ISBN: 0262535432 Category : Computers Languages : en Pages : 282
Book Description
A concise introduction to the emerging field of data science, explaining its evolution, relation to machine learning, current uses, data infrastructure issues, and ethical challenges. The goal of data science is to improve decision making through the analysis of data. Today data science determines the ads we see online, the books and movies that are recommended to us online, which emails are filtered into our spam folders, and even how much we pay for health insurance. This volume in the MIT Press Essential Knowledge series offers a concise introduction to the emerging field of data science, explaining its evolution, current uses, data infrastructure issues, and ethical challenges. It has never been easier for organizations to gather, store, and process data. Use of data science is driven by the rise of big data and social media, the development of high-performance computing, and the emergence of such powerful methods for data analysis and modeling as deep learning. Data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting non-obvious and useful patterns from large datasets. It is closely related to the fields of data mining and machine learning, but broader in scope. This book offers a brief history of the field, introduces fundamental data concepts, and describes the stages in a data science project. It considers data infrastructure and the challenges posed by integrating data from multiple sources, introduces the basics of machine learning, and discusses how to link machine learning expertise with real-world problems. The book also reviews ethical and legal issues, developments in data regulation, and computational approaches to preserving privacy. Finally, it considers the future impact of data science and offers principles for success in data science projects.
Author: United States. Congress. House. Committee on Appropriations. Subcommittee on VA, HUD, and Independent Agencies Publisher: ISBN: Category : United States Languages : en Pages : 1116
Author: D. Jewitt Publisher: Springer Science & Business Media ISBN: 354071958X Category : Science Languages : en Pages : 258
Book Description
The study of the Solar system, particularly of its newly discovered outer parts, is one of the hottest topics in modern astrophysics with great potential for revealing fundamental clues about the origin of planets and even the emergence of life. The three lecturers of the 35th Saas-Fee Advanced Course, which have been updated and collected in this volume, cover the field from observational, theoretical and numerical perspectives.
Author: Angelica Lo Duca
Author: Angelica Lo Duca
Author: United States. Congress. House. Committee on Appropriations. Subcommittee on Commerce, Justice, Science, and Related Agencies
Author: Michael Grewing
Author: United States. Congress. House. Committee on Appropriations. Subcommittee on Commerce, Justice, Science, and Related Agencies
Author: John D. Kelleher
Author: United States. Congress. House. Committee on Appropriations. Subcommittee on VA, HUD, and Independent Agencies
Author: United States. Congress. Senate. Committee on Aeronautical and Space Sciences
Author: D. Jewitt
Author: 
Author: Harvard University. Library