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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
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
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
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
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
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
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
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
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