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BL
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| Record Number
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851089
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| Main Entry
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Salvaris, Mathew
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| Title & Author
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Deep learning with Azure : : building and deploying artificial intelligence solutions on the Microsoft AI platform /\ Mathew Salvaris, Danielle Dean, Wee Hyong Tok.
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| Publication Statement
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New York :: Apress,, 2018.
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| Page. NO
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1 online resource
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| ISBN
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1484236793
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: 1484236807
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: 9781484236796
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: 9781484236802
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1484236785
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9781484236789
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| Bibliographies/Indexes
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Includes bibliographical references and index.
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| Contents
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Intro; Table of Contents; About the Authors; About the Guest Authors of Chapter 7; About the Technical Reviewers; Acknowledgments; Foreword; Introduction; Part I: Getting Started with AI; Chapter 2: Overview of Deep Learning; Common Network Structures; Convolutional Neural Networks; Recurrent Neural Networks; Generative Adversarial Networks; Autoencoders; Deep Learning Workflow; Finding Relevant Data Set(s); Data Set Preprocessing; Training the Model; Validating and Tuning the Model; Deploy the Model; Deep Learning Frameworks & Compute.
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Azure Machine Learning StudioIntegrated Development Environments; Deep Learning Frameworks; Broader Azure Platform; Getting Started with the Deep Learning Virtual Machine; Running the Notebook Server; Summary; Chapter 5: Cognitive Services and Custom Vision; Prebuilt AI: Why and How?; Cognitive Services; What Types of Cognitive Services Are Available?; Computer Vision APIs; How to Use Optical Character Recognition-; How to Recognize Celebrities and Landmarks; How Do I Get Started with Cognitive Services?; Custom Vision; Hello World! for Custom Vision; Exporting Custom Vision Models; Summary.
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Jump Start Deep Learning: Transfer Learning and Domain AdaptationModels Library; Summary; Chapter 3: Trends in Deep Learning; Variations on Network Architectures; Residual Networks and Variants; DenseNet; Small Models, Fewer Parameters; Capsule Networks; Object Detection; Object Segmentation; More Sophisticated Networks; Automated Machine Learning; Hardware; More Specialized Hardware; Hardware on Azure; Quantum Computing; Limitations of Deep Learning; Be Wary of Hype; Limits on Ability to Generalize; Data Hungry Models, Especially Labels; Reproducible Research and Underlying Theory.
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Looking Ahead: What Can We Expect from Deep Learning?Ethics and Regulations; Summary; Chapter 1: Introduction to Artificial Intelligence; Microsoft and AI; Machine Learning; Deep Learning; Rise of Deep Learning; Applications of Deep Learning; Summary; Part II: Azure AI Platform and Experimentation Tools; Chapter 4: Microsoft AI Platform; Services; Prebuilt AI: Cognitive Services; Conversational AI: Bot Framework; Custom AI: Azure Machine Learning Services; Custom AI: Batch AI; Infrastructure; Data Science Virtual Machine; Spark; Container Hosting; Data Storage; Tools.
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Part III: AI Networks in PracticeChapter 6: Convolutional Neural Networks; The Convolution in Convolution Neural Networks; Convolution Layer; Pooling Layer; Activation Functions; Sigmoid; Tanh; Rectified Linear Unit; CNN Architecture; Training Classification CNN; Why CNNs; Training CNN on CIFAR10; Training a Deep CNN on GPU; Model 1; Model 2; Model 3; Model 4; Transfer Learning; Summary; Chapter 7: Recurrent Neural Networks; RNN Architectures; Training RNNs; Gated RNNs; Sequence-to-Sequence Models and Attention Mechanism; RNN Examples; Example 1: Sentiment Analysis.
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| Abstract
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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 LearnBecome familiar with the tools, infrastructure, and services available for deep learning on Microsoft Azure such as Azure Machine Learning services and Batch AIUse 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 evolvingDiscover the options for training and operationalizing deep learning models on Azure Who This Book Is ForProfessional 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.
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| Subject
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Microsoft Azure (Computing platform)
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COMPUTERS-- Computer Literacy.
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COMPUTERS-- Computer Science.
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COMPUTERS-- Data Processing.
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COMPUTERS-- Hardware-- General.
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COMPUTERS-- Information Technology.
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COMPUTERS-- Machine Theory.
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COMPUTERS-- Reference.
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Microsoft Azure (Computing platform)
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Microsoft programming.
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Program concepts-- learning to program.
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| Dewey Classification
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004.67/82
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| LC Classification
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QA76.585.S25 2018eb
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| Added Entry
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Dean, Danielle
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Tok, Wee-Hyong
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