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Deep learning with Azurebuilding and...

Dean, Danielle.

Deep learning with Azurebuilding and deploying artificial intelligence solutions on the Microsoft AI platform /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep learning with Azureby Mathew Salvaris, Danielle Dean, Wee Hyong Tok.
Reminder of title:	building and deploying artificial intelligence solutions on the Microsoft AI platform /
Author:	Salvaris, Mathew.
other author:	Dean, Danielle.
Published:	Berkeley, CA :Apress :2018.
Description:	xxvii, 284 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Microsoft Azure (Computing platform)
Online resource:	https://doi.org/10.1007/978-1-4842-3679-6
ISBN:	9781484236796$q(electronic bk.)

Deep learning with Azurebuilding 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. - Berkeley, CA :Apress :2018. - xxvii, 284 p. :ill., digital ;24 cm.

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$q(electronic bk.)

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

763318
Microsoft Azure (Computing platform)

LC Class. No.: QA76.585 / .S258 2018

Dewey Class. No.: 004.6782

Deep learning with Azurebuilding and deploying artificial intelligence solutions on the Microsoft AI platform /
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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.
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