國立虎尾科技大學 |

Deep Learning with Azure = Building and Deploying Artificial Intelligence Solutions on the Microsoft AI Platform /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Deep Learning with Azure/ by Mathew Salvaris, Danielle Dean, Wee Hyong Tok.
其他題名:	Building and Deploying Artificial Intelligence Solutions on the Microsoft AI Platform /
作者:	Salvaris, Mathew.
其他作者:	Dean, Danielle.
面頁冊數:	XXVII, 284 p. 104 illus.online resource. :
Contained By:	Springer Nature eBook
標題:	Microsoft software. -
電子資源:	https://doi.org/10.1007/978-1-4842-3679-6
ISBN:	9781484236796

Deep Learning with Azure = Building and Deploying Artificial Intelligence Solutions on the Microsoft AI Platform /
Salvaris, Mathew.

Deep Learning with AzureBuilding and Deploying Artificial Intelligence Solutions on the Microsoft AI Platform /[electronic resource] :by Mathew Salvaris, Danielle Dean, Wee Hyong Tok. - 1st ed. 2018. - XXVII, 284 p. 104 illus.online resource.

Part 1 - Getting Started with AI -- Chapter 1: Introduction to Artificial Intelligence -- Chapter 2: Overview of Deep Learning -- Chapter 3: Trends in Deep Learning -- Part 2: Azure AI Platform and Experimentation Tools -- Chapter 4: Microsoft AI Platform -- Chapter 5: Cognitive Services and Custom Vision -- Part 3: AI Networks in Practice -- Chapter 6: Convolutional Neural Networks -- Chapter 7: Recurrent Neural Networks -- Chapter 8: Generative Adversarial Networks (GANs) -- Part 4: AI Architectures and Best Practices -- Chapter 9: Training AI Models -- Chapter 10: Operationalizing AI Models -- Appendix: Notes.

Get up-to-speed with Microsoft's AI Platform. Learn to innovate and accelerate with open and powerful tools and services that bring artificial intelligence to every data scientist and developer. Artificial Intelligence (AI) is the new normal. Innovations in deep learning algorithms and hardware are happening at a rapid pace. It is no longer a question of should I build AI into my business, but more about where do I begin and how do I get started with AI? Written by expert data scientists at Microsoft, Deep Learning with the Microsoft AI Platform helps you with the how-to of doing deep learning on Azure and leveraging deep learning to create innovative and intelligent solutions. Benefit from guidance on where to begin your AI adventure, and learn how the cloud provides you with all the tools, infrastructure, and services you need to do AI. What You'll Learn: Become familiar with the tools, infrastructure, and services available for deep learning on Microsoft Azure such as Azure Machine Learning services and Batch AI Use pre-built AI capabilities (Computer Vision, OCR, gender, emotion, landmark detection, and more) Understand the common deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs) with sample code and understand how the field is evolving Discover the options for training and operationalizing deep learning models on Azure This book is for professional data scientists who are interested in learning more about deep learning and how to use the Microsoft AI platform. Some experience with Python is helpful. Mathew Salvaris, PhD is a senior data scientist at Microsoft in the Cloud and AI division, where he works with a team of data scientists and engineers building machine learning and AI solutions for external companies utilizing Microsoft's Cloud AI platform. Danielle Dean, PhD is a principal data science lead at Microsoft in the Cloud and AI division, where she leads a team of data scientists and engineers building artificial intelligence solutions with external companies utilizing Microsoft’s Cloud AI platform. Wee Hyong Tok, PhD is a principal data science manager at Microsoft in the Cloud and AI division. He leads the AI for Earth Engineering and Data Science team, where his team of data scientists and engineers are working to advance the boundaries of state-of-the-art deep learning algorithms and systems.

ISBN: 9781484236796

Standard No.: 10.1007/978-1-4842-3679-6doiSubjects--Topical Terms:

1253736
Microsoft software.

LC Class. No.: QA76.76.M52

Dewey Class. No.: 004.165

Deep Learning with Azure = Building and Deploying Artificial Intelligence Solutions on the Microsoft AI Platform /
LDR:04497nam a22003975i 4500 001 988260
003 DE-He213
005 20200702153324.0
007 cr nn 008mamaa
008 201225s2018 xxu| s |||| 0|eng d
020 $a 9781484236796 $9 978-1-4842-3679-6
024 7 $a 10.1007/978-1-4842-3679-6 $2 doi
035 $a 978-1-4842-3679-6
050 4 $a QA76.76.M52
072 7 $a UMP $2 bicssc
072 7 $a COM051380 $2 bisacsh
072 7 $a UMP $2 thema
082 0 4 $a 004.165 $2 23
100 1 $a Salvaris, Mathew. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1208922
245 1 0 $a Deep Learning with Azure $h [electronic resource] : $b Building and Deploying Artificial Intelligence Solutions on the Microsoft AI Platform / $c by Mathew Salvaris, Danielle Dean, Wee Hyong Tok.
250 $a 1st ed. 2018.
264 1 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2018.
300 $a XXVII, 284 p. 104 illus. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
505 0 $a Part 1 - Getting Started with AI -- Chapter 1: Introduction to Artificial Intelligence -- Chapter 2: Overview of Deep Learning -- Chapter 3: Trends in Deep Learning -- Part 2: Azure AI Platform and Experimentation Tools -- Chapter 4: Microsoft AI Platform -- Chapter 5: Cognitive Services and Custom Vision -- Part 3: AI Networks in Practice -- Chapter 6: Convolutional Neural Networks -- Chapter 7: Recurrent Neural Networks -- Chapter 8: Generative Adversarial Networks (GANs) -- Part 4: AI Architectures and Best Practices -- Chapter 9: Training AI Models -- Chapter 10: Operationalizing AI Models -- Appendix: Notes.
520 $a Get up-to-speed with Microsoft's AI Platform. Learn to innovate and accelerate with open and powerful tools and services that bring artificial intelligence to every data scientist and developer. Artificial Intelligence (AI) is the new normal. Innovations in deep learning algorithms and hardware are happening at a rapid pace. It is no longer a question of should I build AI into my business, but more about where do I begin and how do I get started with AI? Written by expert data scientists at Microsoft, Deep Learning with the Microsoft AI Platform helps you with the how-to of doing deep learning on Azure and leveraging deep learning to create innovative and intelligent solutions. Benefit from guidance on where to begin your AI adventure, and learn how the cloud provides you with all the tools, infrastructure, and services you need to do AI. What You'll Learn: Become familiar with the tools, infrastructure, and services available for deep learning on Microsoft Azure such as Azure Machine Learning services and Batch AI Use pre-built AI capabilities (Computer Vision, OCR, gender, emotion, landmark detection, and more) Understand the common deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs) with sample code and understand how the field is evolving Discover the options for training and operationalizing deep learning models on Azure This book is for professional data scientists who are interested in learning more about deep learning and how to use the Microsoft AI platform. Some experience with Python is helpful. Mathew Salvaris, PhD is a senior data scientist at Microsoft in the Cloud and AI division, where he works with a team of data scientists and engineers building machine learning and AI solutions for external companies utilizing Microsoft's Cloud AI platform. Danielle Dean, PhD is a principal data science lead at Microsoft in the Cloud and AI division, where she leads a team of data scientists and engineers building artificial intelligence solutions with external companies utilizing Microsoft’s Cloud AI platform. Wee Hyong Tok, PhD is a principal data science manager at Microsoft in the Cloud and AI division. He leads the AI for Earth Engineering and Data Science team, where his team of data scientists and engineers are working to advance the boundaries of state-of-the-art deep learning algorithms and systems.
650 0 $a Microsoft software. $3 1253736
650 0 $a Microsoft .NET Framework. $3 565417
650 0 $a Artificial intelligence. $3 559380
650 1 4 $a Microsoft and .NET. $3 1114109
650 2 4 $a Artificial Intelligence. $3 646849
700 1 $a Dean, Danielle. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1208923
700 1 $a Tok, Wee Hyong. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1068174
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9781484236789
776 0 8 $i Printed edition: $z 9781484236802
776 0 8 $i Printed edition: $z 9781484247754
856 4 0 $u https://doi.org/10.1007/978-1-4842-3679-6
912 $a ZDB-2-CWD
912 $a ZDB-2-SXPC
950 $a Professional and Applied Computing (SpringerNature-12059)
950 $a Professional and Applied Computing (R0) (SpringerNature-43716)