標(biāo)題: Titlebook: Data Science Solutions on Azure; Tools and Techniques Julian Soh,Priyanshi Singh Book 20201st edition Julian Soh and Priyanshi Singh 2020 A [打印本頁] 作者: 郊區(qū) 時間: 2025-3-21 16:54
書目名稱Data Science Solutions on Azure影響因子(影響力)
書目名稱Data Science Solutions on Azure影響因子(影響力)學(xué)科排名
書目名稱Data Science Solutions on Azure網(wǎng)絡(luò)公開度
書目名稱Data Science Solutions on Azure網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Data Science Solutions on Azure被引頻次
書目名稱Data Science Solutions on Azure被引頻次學(xué)科排名
書目名稱Data Science Solutions on Azure年度引用
書目名稱Data Science Solutions on Azure年度引用學(xué)科排名
書目名稱Data Science Solutions on Azure讀者反饋
書目名稱Data Science Solutions on Azure讀者反饋學(xué)科排名
作者: sperse 時間: 2025-3-21 20:26
Government Regulation of Transfer PricingThe exponential pace of innovation in artificial intelligence in recent years can be attributed to advancements in machine learning. In turn, the advancements in machine learning are based on two core developments?– availability of data and ubiquitous access to unparalleled compute capabilities.作者: 激怒某人 時間: 2025-3-22 00:47
https://doi.org/10.1007/978-3-030-58823-6In Chapter ., we explored the concepts of Spark and Azure Databricks’ implementation of the platform. In this chapter, we will be doing a hands-on exploration of these concepts in Azure Databricks.作者: 女歌星 時間: 2025-3-22 08:31 作者: Expertise 時間: 2025-3-22 10:12 作者: adhesive 時間: 2025-3-22 14:31
Hands-on with Azure Databricks,In Chapter ., we explored the concepts of Spark and Azure Databricks’ implementation of the platform. In this chapter, we will be doing a hands-on exploration of these concepts in Azure Databricks.作者: adhesive 時間: 2025-3-22 20:28
Data Science in the Modern Enterprise,t innovation, such as machine learning (ML), artificial intelligence (AI), and Internet of Things (IoT). This is not an inaccurate representation since data science is after all the foundation for ML, AI, and IoT.作者: 抱狗不敢前 時間: 2025-3-23 00:30
Data Preparation and Data Engineering Basics,he process, which, if not done correctly, would yield inaccurate results and may lead to negative consequences. That is why so much time is being spent on data preparation. If we want to make the data science process more efficient, shaving off the amount of time spent on data preparation is one area for us to look at.作者: Ceremony 時間: 2025-3-23 04:55
Clinical Diagnosis: “Simple” Patientst innovation, such as machine learning (ML), artificial intelligence (AI), and Internet of Things (IoT). This is not an inaccurate representation since data science is after all the foundation for ML, AI, and IoT.作者: Watemelon 時間: 2025-3-23 08:14 作者: glans-penis 時間: 2025-3-23 10:46
https://doi.org/10.1007/978-1-4842-6405-8Azure; Data Scientist; DevOps; Azure Databricks; data abstraction; Big data analytics作者: 后退 時間: 2025-3-23 15:33
978-1-4842-6404-1Julian Soh and Priyanshi Singh 2020作者: 機構(gòu) 時間: 2025-3-23 20:58
Clinical Diagnosis: “Simple” Patientst innovation, such as machine learning (ML), artificial intelligence (AI), and Internet of Things (IoT). This is not an inaccurate representation since data science is after all the foundation for ML, AI, and IoT.作者: crockery 時間: 2025-3-24 01:07 作者: adj憂郁的 時間: 2025-3-24 04:54 作者: 從屬 時間: 2025-3-24 09:42 作者: Mutter 時間: 2025-3-24 11:02
https://doi.org/10.1007/978-3-030-44703-8livery and quality of model development through monitoring, validation, and governance of machine learning models. This is equivalent to how DevOps helps software engineers develop, test, and deploy software quicker and with fewer defects. MLOps supports the data science life cycle just as DevOps su作者: 人造 時間: 2025-3-24 18:48 作者: CLAY 時間: 2025-3-24 21:07 作者: painkillers 時間: 2025-3-25 02:32
Statistical Techniques and Concepts in Data Science,e been brought together to solve a business problem, optimize a process, or create predictive models based on data-driven techniques. It is thus imperative that all members of the team have some idea of the statistical techniques and concepts used in data science.作者: 減去 時間: 2025-3-25 06:46
Book 20201st editiony, and help drive the transformation of organizations into a knowledge and data-driven entity.?It?provides an end-to-end understanding of data science life cycle and the techniques to efficiently productionize workloads.?.The book starts with an introduction to data science and discusses the statist作者: Archipelago 時間: 2025-3-25 11:33
Hands-on with Azure Machine Learning,ython SDK, R SDK, and low-code or zero-code Azure ML designer approaches to develop, train, and deploy ML models; we will use Python SDK for our hands-on labs in this chapter. For the purposes of hands-on lab in this chapter, we will assume users are familiar with Python and getting started with implementing data science solutions on cloud.作者: Amylase 時間: 2025-3-25 12:22 作者: 大量 時間: 2025-3-25 17:43
Parallel Processes in Oculomotor Controle been brought together to solve a business problem, optimize a process, or create predictive models based on data-driven techniques. It is thus imperative that all members of the team have some idea of the statistical techniques and concepts used in data science.作者: 招人嫉妒 時間: 2025-3-25 23:29
operation discussedUnderstand and learn?the skills needed to use modern tools in Microsoft Azure. This book discusses how to practically apply these tools in the industry, and help drive the transformation of organizations into a knowledge and data-driven entity.?It?provides an end-to-end understan作者: 種植,培養(yǎng) 時間: 2025-3-26 01:19 作者: Osmosis 時間: 2025-3-26 04:46 作者: 出生 時間: 2025-3-26 11:09 作者: 嗎啡 時間: 2025-3-26 15:02
Statistical Techniques and Concepts in Data Science,e of the math used in statistical techniques. The “data scientist” today may be a transitioning database professionals, data/Big Data engineers, software engineer, IT auditor, fraud investigator, or even a business analyst. Often, a project team would be comprised of all these professionals that hav作者: 壯麗的去 時間: 2025-3-26 19:48 作者: 祝賀 時間: 2025-3-27 00:49 作者: 食物 時間: 2025-3-27 05:09
Machine Learning Operations,livery and quality of model development through monitoring, validation, and governance of machine learning models. This is equivalent to how DevOps helps software engineers develop, test, and deploy software quicker and with fewer defects. MLOps supports the data science life cycle just as DevOps su作者: incarcerate 時間: 2025-3-27 08:23
ing real life scenarios and how-to-build solutions in a single comprehensive cloud ecosystem.?.What You‘ll Learn.Understand big data analytics with Spark in Azure Databri978-1-4842-6404-1978-1-4842-6405-8作者: 粉筆 時間: 2025-3-27 12:15 作者: watertight, 時間: 2025-3-27 15:05 作者: 2否定 時間: 2025-3-27 20:15 作者: 不溶解 時間: 2025-3-27 22:03 作者: Ovulation 時間: 2025-3-28 05:18 作者: extemporaneous 時間: 2025-3-28 10:18 作者: manifestation 時間: 2025-3-28 10:41
Parametric AR(p)-ARCH(q) Models,eters by the ordinary least squares (OLS) method and adopt the two-step estimation for the ARCH part, in which the parameters of the ARCH part are estimated based on the residuals of the AR part. In the first section we sketch the estimation theory for the parametric AR (.)-ARCH (.) model with the O作者: 微不足道 時間: 2025-3-28 16:15 作者: 群島 時間: 2025-3-28 19:52
