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“…You'll not only learn the basics -- how to pool resources from hardware servers, computer clusters, networks, and storage, and then distribute them among virtual machines -- but also the stumbling blocks you'll encounter when you monitor systems, troubleshoot problems, and deal with security. …”

“…Install, configure, and manage all Oracle VM components Plan, size, and set up Oracle VM server farms and server pools Control resources from Oracle Enterprise Manager Grid Control, Oracle VM Manager, and Oracle VM Command Line Interface Govern network drives and virtual storage using Oracle VM tools Create virtual machines manually or from Oracle library templates Convert existing virtual machines on other systems to Oracle VM virtual machines Generate virtual machine clones that run on multiple server pools Maintain guest operating systems and software using Oracle Enterprise Manager Grid Control's Oracle VM Management Pack…”

Tabla de Contenidos: “…4.6.3 Hierarchical Pathfinding on Exclusions -- 4.6.4 Strange Effects of Hierarchies on Pathfinding -- 4.6.5 Instanced Geometry -- 4.7 Other Ideas in Pathfinding -- 4.7.1 Open Goal Pathfinding -- 4.7.2 Dynamic Pathfinding -- 4.7.3 Other Kinds of Information Reuse -- 4.7.4 Low Memory Algorithms -- 4.7.5 Interruptible Pathfinding -- 4.7.6 Pooling Planners -- 4.8 Continuous Time Pathfinding -- 4.8.1 The Problem -- 4.8.2 The Algorithm -- 4.8.3 Implementation Notes -- 4.8.4 Performance -- 4.8.5 Weaknesses -- 4.9 Movement Planning -- 4.9.1 Animations -- 4.9.2 Movement Planning -- 4.9.3 Example -- 4.9.4 Footfalls -- CHAPTER 5: DECISION MAKING -- 5.1 Overview of Decision Making -- 5.2 Decision Trees -- 5.2.1 The Problem -- 5.2.2 The Algorithm -- 5.2.3 Pseudo-Code -- 5.2.4 Knowledge Representation -- 5.2.5 Implementation Notes -- 5.2.6 Performance of Decision Trees -- 5.2.7 Balancing the Tree -- 5.2.8 Beyond the Tree -- 5.2.9 Random Decision Trees -- 5.3 State Machines -- 5.3.1 The Problem -- 5.3.2 The Algorithm -- 5.3.3 Pseudo-Code -- 5.3.4 Data Structures and Interfaces -- 5.3.5 Performance -- 5.3.6 Implementation Notes -- 5.3.7 Hard-Coded FSM -- 5.3.8 Hierarchical State Machines -- 5.3.9 Combining Decision Trees and State Machines -- 5.4 Behavior Trees -- 5.4.1 Implementing Behavior Trees -- 5.4.2 Pseudo-Code -- 5.4.3 Decorators -- 5.4.4 Concurrency and Timing -- 5.4.5 Adding Data to Behavior Trees -- 5.4.6 Reusing Trees -- 5.4.7 Limitations of Behavior Trees -- 5.5 Fuzzy Logic -- 5.5.1 A Warning -- 5.5.2 Introduction to Fuzzy Logic -- 5.5.3 Fuzzy Logic Decision Making -- 5.5.4 Fuzzy State Machines -- 5.6 Markov Systems -- 5.6.1 Markov Processes -- 5.6.2 Markov State Machine -- 5.7 Goal-Oriented Behavior -- 5.7.1 Goal-Oriented Behavior -- 5.7.2 Simple Selection -- 5.7.3 Overall Utility -- 5.7.4 Timing -- 5.7.5 Overall Utility GOAP -- 5.7.6 GOAP with IDA*…”

Tabla de Contenidos: “…8.8 Summary -- Chapter 9: Overfitting -- 9.1 Model Capacity -- 9.2 Overfitting and Underfitting -- 9.2.1 Underfitting -- 9.2.2 Overfitting -- 9.3 Dataset Division -- 9.3.1 Validation Set and Hyperparameters -- 9.3.2 Early Stopping -- 9.4 Model Design -- 9.5 Regularization -- 9.5.1 L0 Regularization -- 9.5.2 L1 Regularization -- 9.5.3 L2 Regularization -- 9.5.4 Regularization Effect -- 9.6 Dropout -- 9.7 Data Augmentation -- 9.7.1 Rotation -- 9.7.2 Flip -- 9.7.3 Cropping -- 9.7.4 Generate Data -- 9.7.5 Other Methods -- 9.8 Hands-On Overfitting -- 9.8.1 Build the Dataset -- 9.8.2 Influence of the Number of Network Layers -- 9.8.3 Impact of Dropout -- 9.8.4 Impact of Regularization -- 9.9 References -- Chapter 10: Convolutional Neural Networks -- 10.1 Problems with Fully Connected N -- 10.1.1 Local Correlation -- 10.1.2 Weight Sharing -- 10.1.3 Convolution Operation -- 10.2 Convolutional Neural Network -- 10.2.1 Single-Channel Input and Single Convolution Kernel -- 10.2.2 Multi-channel Input and Single Convolution Kernel -- 10.2.3 Multi-channel Input and Multi-convolution Kernel -- 10.2.4 Stride Size -- 10.2.5 Padding -- 10.3 Convolutional Layer Implementation -- 10.3.1 Custom Weights -- 10.3.2 Convolutional Layer Classes -- 10.4 Hands-On LeNet-5 -- 10.5 Representation Learning -- 10.6 Gradient Propagation -- 10.7 Pooling Layer -- 10.8 BatchNorm Layer -- 10.8.1 Forward Propagation -- 10.8.2 Backward Propagation -- 10.8.3 Implementation of BatchNormalization layer -- 10.9 Classical Convolutional Network -- 10.9.1 AlexNet -- 10.9.2 VGG Series -- 10.9.3 GoogLeNet -- 10.10 Hands-On CIFAR10 and VGG13 -- 10.11 Convolutional Layer Variants -- 10.11.1 Dilated/Atrous Convolution -- 10.11.2 Transposed Convolution -- o + 2p − k = n * s -- o + 2p − k ≠n * s -- Matrix Transposition -- Transposed Convolution Implementation -- 10.11.3 Separate Convolution…”

Tabla de Contenidos: “…5.3.1 FTI SPAM Using GA Algorithm -- 5.3.1.1 Chromosome Generation -- 5.3.1.2 Fitness Function -- 5.3.1.3 Crossover -- 5.3.1.4 Mutation -- 5.3.1.5 Termination -- 5.3.2 Patterns Matching Using SCI -- 5.3.3 Pattern Classification Based on SCI Value -- 5.3.4 Significant Pattern Evaluation -- 5.4 Detection of Congestive Heart Failure Using Automatic Classifier -- 5.4.1 Analyzing the Dataset -- 5.4.2 Data Collection -- 5.4.2.1 Long-Term HRV Measures -- 5.4.2.2 Attribute Selection -- 5.4.3 Automatic Classifier-Belief Network -- 5.5 Experimental Analysis -- 5.6 Conclusion -- References -- 6 Multimodal Context-Sensitive Human Communication Interaction System Using Artificial Intelligence-Based Human-Centered Computing -- 6.1 Introduction -- 6.2 Literature Survey -- 6.3 Proposed Model -- 6.3.1 Multimodal Data -- 6.3.2 Dimensionality Reduction -- 6.3.3 Principal Component Analysis -- 6.3.4 Reduce the Number of Dimensions -- 6.3.5 CNN -- 6.3.6 CNN Layers -- 6.3.6.1 Convolution Layers -- 6.3.6.2 Padding Layer -- 6.3.6.3 Pooling/Subsampling Layers -- 6.3.6.4 Nonlinear Layers -- 6.3.7 ReLU -- 6.3.7.1 Fully Connected Layers -- 6.3.7.2 Activation Layer -- 6.3.8 LSTM -- 6.3.9 Weighted Combination of Networks -- 6.4 Experimental Results -- 6.4.1 Accuracy -- 6.4.2 Sensibility -- 6.4.3 Specificity -- 6.4.4 A Predictive Positive Value (PPV) -- 6.4.5 Negative Predictive Value (NPV) -- 6.5 Conclusion -- 6.6 Future Scope -- References -- 7 AI, Planning and Control Algorithms for IoRT Systems -- 7.1 Introduction -- 7.2 General Architecture of IoRT -- 7.2.1 Hardware Layer -- 7.2.2 Network Layer -- 7.2.3 Internet Layer -- 7.2.4 Infrastructure Layer -- 7.2.5 Application Layer -- 7.3 Artificial Intelligence in IoRT Systems -- 7.3.1 Technologies of Robotic Things -- 7.3.2 Artificial Intelligence in IoRT -- 7.4 Control Algorithms and Procedures for IoRT Systems…”

Tabla de Contenidos: “…Instance and database operations -- 4.1 Instance operation modes -- 4.1.1 Online mode -- 4.1.2 Offline mode -- 4.1.3 Quiescent mode -- 4.1.4 Creating and dropping the instance -- 4.2 Modifying the configuration -- 4.2.1 Working with the DAS -- 4.2.2 Viewing or updating the configuration using Control Center -- 4.2.3 Managing database partition groups -- 4.2.4 Managing buffer pools -- 4.3 Managing database storage -- 4.3.1 Table spaces and containers -- 4.3.2 Monitoring table space and container storage -- 4.3.3 Transactions and logs -- 4.4 Backup and recovery -- 4.4.1 Recovery types -- 4.4.2 Backup and restore methods -- 4.4.3 Table level restore -- 4.5 High availability -- 4.5.1 Log mirroring -- 4.5.2 Replication -- 4.5.3 Online split mirror and suspended I/O support -- 4.6 Security -- 4.6.1 Authorization and privileges -- 4.6.2 Roles and groups -- 4.6.3 Security levels -- 4.6.4 Client/server security -- 4.6.5 Authentication methods -- Chapter 5. …”

Tabla de Contenidos: “…9.1.1 Azure Cognitive Services -- 9.1.2 Managing assemblies in the Data Lake -- 9.1.3 Image data extraction with Advanced Analytics -- 9.2 Reading different file types -- 9.2.1 Adding custom libraries with a Catalog -- 9.2.2 Creating a catalog database -- 9.2.3 Building the U-SQL DataFormats solution -- 9.2.4 Code folders -- 9.2.5 Using custom assemblies -- 9.3 Connecting to remote sources -- 9.3.1 External databases -- 9.3.2 Credentials -- 9.3.3 Data Source -- 9.3.4 Tables and views -- 9.4 Exercises -- 9.4.1 Exercise 1 -- 9.4.2 Exercise 2 -- Summary -- 10 Service integration with Azure Data Factory -- 10.1 Creating an Azure Data Factory service -- 10.2 Secure authentication -- 10.2.1 Azure Active Directory integration -- 10.2.2 Azure Key Vault -- 10.3 Copying files with ADF -- 10.3.1 Creating a Files storage container -- 10.3.2 Adding secrets to AKV -- 10.3.3 Creating a Files storage linkedservice -- 10.3.4 Creating an ADLS linkedservice -- 10.3.5 Creating a pipeline and activity -- 10.3.6 Creating a scheduled trigger -- 10.4 Running an ADLA job -- 10.4.1 Creating an ADLA linkedservice -- 10.4.2 Creating a pipeline and activity -- 10.5 Exercises -- 10.5.1 Exercise 1 -- 10.5.2 Exercise 2 -- Summary -- 11 Managed SQL with Azure SQL Database -- 11.1 Creating an Azure SQL Database -- 11.1.1 Create a SQL Server and SQLDB -- 11.2 Securing SQLDB -- 11.3 Availability and recovery -- 11.3.1 Restoring and moving SQLDB -- 11.3.2 Database safeguards -- 11.3.3 Creating alerts for SQLDB -- 11.4 Optimizing costs for SQLDB -- 11.4.1 Pricing structure -- 11.4.2 Scaling SQLDB -- 11.4.3 Serverless -- 11.4.4 Elastic Pools -- 11.5 Exercises -- 11.5.1 Exercise 1 -- 11.5.2 Exercise 2 -- 11.5.3 Exercise 3 -- 11.5.4 Exercise 4 -- Summary -- 12 Integrating Data Factory with SQL Database -- 12.1 Before you begin -- 12.2 Importing data with external data sources…”

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“…Finally, we devote a chapter to the migration of applications from the pooled JVM model to the new JVM server run time…”

“…Tunnelling has become a fragmented process, excessively influenced by lawyers'notions of confrontational contractual bases. This prevents the pooling of skills, essential to the achievement of the promoters' objectives. …”

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