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Advanced Blazor Architecture with . NET 10 and Azure OpenAI : Enterprise-Grade Design Patterns, AI Integration, and Cloud-Ready Solutions
| Advanced Blazor Architecture with . NET 10 and Azure OpenAI : Enterprise-Grade Design Patterns, AI Integration, and Cloud-Ready Solutions |
| Autore | Prasad Amit |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Berkeley, CA : , : Apress L. P., , 2026 |
| Descrizione fisica | 1 online resource (269 pages) |
| Collana | Professional and Applied Computing Series |
| ISBN | 9798868825187 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Advanced Blazor Architecture with .NET 10 and Azure OpenAI -- Introduction -- Acknowledgments -- Table of Contents -- About the Authors -- About the Technical Reviewer -- 1. Introduction to Modern Blazor -- 2. .NET 10 Enhancements for Blazor -- 3. Enterprise-Grade Blazor Architecture -- 4. Micro-Frontends and Modular UI -- 5. Event-Driven and Reactive Patterns -- 6. Introducing Azure OpenAI in Blazor -- 7. Practical AI Features in Blazor -- 8. Secure AI Prompt Handling -- 9. Deploying Blazor at Scale -- 10. CI/CD for Blazor and AI Apps -- 11. Cloud-Native Observability and Resilience -- 12. Optimizing Performance in Blazor Apps -- 13. Zero-Trust and Enterprise Security -- 14. Case Study: AI-Enhanced Enterprise App -- 15. Future of Blazor and AI in Enterprise Solutions -- References -- Index. |
| Record Nr. | UNINA-9911124360603321 |
Prasad Amit
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| Berkeley, CA : , : Apress L. P., , 2026 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Advanced Forecasting with Python : With State-Of-the-Art-Models Including LSTMs, Facebook's Prophet, and Amazon's DeepAR
| Advanced Forecasting with Python : With State-Of-the-Art-Models Including LSTMs, Facebook's Prophet, and Amazon's DeepAR |
| Autore | Korstanje Joos |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Berkeley, CA : , : Apress L. P., , 2021 |
| Descrizione fisica | 1 online resource (294 pages) |
| Disciplina | 006.31 |
| Collana | Professional and Applied Computing Series |
| Soggetto non controllato | Science |
| ISBN |
9781484271506
1484271505 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Table of Contents -- About the Author -- About the Technical Reviewer -- Introduction -- Part I: Machine Learning for Forecasting -- Chapter 1: Models for Forecasting -- Reading Guide for This Book -- Machine Learning Landscape -- Univariate Time Series Models -- A Quick Example of the Time Series Approach -- Supervised Machine Learning Models -- A Quick Example of the Supervised Machine Learning Approach -- Correlation Coefficient -- Other Distinctions in Machine Learning Models -- Supervised vs. Unsupervised Models -- Classification vs. Regression Models -- Univariate vs. Multivariate Models -- Key Takeaways -- Chapter 2: Model Evaluation for Forecasting -- Evaluation with an Example Forecast -- Model Quality Metrics -- Metric 1: MSE -- Metric 2: RMSE -- Metric 3: MAE -- Metric 4: MAPE -- Metric 5: R2 -- Model Evaluation Strategies -- Overfit and the Out-of-Sample Error -- Strategy 1: Train-Test Split -- Strategy 2: Train-Validation-Test Split -- Strategy 3: Cross-Validation for Forecasting -- K-Fold Cross-Validation -- Time Series Cross-Validation -- Rolling Time Series Cross-Validation -- Backtesting -- Which Strategy to Use for Safe Forecasts? -- Final Considerations on Model Evaluation -- Key Takeaways -- Part II: Univariate Time Series Models -- Chapter 3: The AR Model -- Autocorrelation: The Past Influences the Present -- Compute Autocorrelation in Earthquake Counts -- Positive and Negative Autocorrelation -- Stationarity and the ADF Test -- Differencing a Time Series -- Lags in Autocorrelation -- Partial Autocorrelation -- How Many Lags to Include? -- AR Model Definition -- Estimating the AR Using Yule-Walker Equations -- The Yule-Walker Method -- Train-Test Evaluation and Tuning -- Key Takeaways -- Chapter 4: The MA Model -- The Model Definition -- Fitting the MA Model -- Stationarity -- Choosing Between an AR and an MA Model.
Application of the MA Model -- Multistep Forecasting with Model Retraining -- Grid Search to Find the Best MA Order -- Key Takeaways -- Chapter 5: The ARMA Model -- The Idea Behind the ARMA Model -- The Mathematical Definition of the ARMA Model -- An Example: Predicting Sunspots Using ARMA -- Fitting an ARMA(1,1) Model -- More Model Evaluation KPIs -- Automated Hyperparameter Tuning -- Grid Search: Tuning for Predictive Performance -- Key Takeaways -- Chapter 6: The ARIMA Model -- ARIMA Model Definition -- Model Definition -- ARIMA on the CO2 Example -- Key Takeaways -- Chapter 7: The SARIMA Model -- Univariate Time Series Model Breakdown -- The SARIMA Model Definition -- Example: SARIMA on Walmart Sales -- Key Takeaways -- Part III: Multivariate Time Series Models -- Chapter 8: The SARIMAX Model -- Time Series Building Blocks -- Model Definition -- Supervised Models vs. SARIMAX -- Example of SARIMAX on the Walmart Dataset -- Key Takeaways -- Chapter 9: The VAR Model -- The Model Definition -- Order: Only One Hyperparameter -- Stationarity -- Estimation of the VAR Coefficients -- One Multivariate Model vs. Multiple Univariate Models -- An Example: VAR for Forecasting Walmart Sales -- Key Takeaways -- Chapter 10: The VARMAX Model -- Model Definition -- Multiple Time Series with Exogenous Variables -- Key Takeaways -- Part IV: Supervised Machine Learning Models -- Chapter 11: The Linear Regression -- The Idea Behind Linear Regression -- Model Definition -- Example: Linear Model to Forecast CO2 Levels -- Key Takeaways -- Chapter 12: The Decision Tree Model -- Mathematics -- Splitting -- Pruning and Reducing Complexity -- Example -- Key Takeaways -- Chapter 13: The kNN Model -- Intuitive Explanation -- Mathematical Definition of Nearest Neighbors -- Combining k Neighbors into One Forecast -- Deciding on the Number of Neighbors k. Predicting Traffic Using kNN -- Grid Search on kNN -- Random Search: An Alternative to Grid Search -- Key Takeaways -- Chapter 14: The Random Forest -- Intuitive Idea Behind Random Forests -- Random Forest Concept 1: Ensemble Learning -- Bagging Concept 1: Bootstrap -- Bagging Concept 2: Aggregation -- Random Forest Concept 2: Variable Subsets -- Predicting Sunspots Using a Random Forest -- Grid Search on the Two Main Hyperparameters of the Random Forest -- Random Search CV Using Distributions -- Distribution for max_features -- Distribution for n_estimators -- Fitting the RandomizedSearchCV -- Interpretation of Random Forests: Feature Importance -- Key Takeaways -- Chapter 15: Gradient Boosting with XGBoost and LightGBM -- Boosting: A Different Way of Ensemble Learning -- The Gradient in Gradient Boosting -- Gradient Boosting Algorithms -- The Difference Between XGBoost and LightGBM -- Forecasting Traffic Volume with XGBoost -- Forecasting Traffic Volume with LightGBM -- Hyperparameter Tuning Using Bayesian Optimization -- The Theory of Bayesian Optimization -- Bayesian Optimization Using scikit-optimize -- Conclusion -- Key Takeaways -- Part V: Advanced Machine and Deep Learning Models -- Chapter 16: Neural Networks -- Fully Connected Neural Networks -- Activation Functions -- The Weights: Backpropagation -- Optimizers -- Learning Rate of the Optimizer -- Hyperparameters at Play in Developing a NN -- Introducing the Example Data -- Specific Data Prep Needs for a NN -- Scaling and Standardization -- Principal Component Analysis (PCA) -- The Neural Network Using Keras -- Conclusion -- Key Takeaways -- Chapter 17: RNNs Using SimpleRNN and GRU -- What Are RNNs: Architecture -- Inside the SimpleRNN Unit -- The Example -- Predicting a Sequence Rather Than a Value -- Univariate Model Rather Than Multivariable -- Preparing the Data -- A Simple SimpleRNN. SimpleRNN with Hidden Layers -- Simple GRU -- GRU with Hidden Layers -- Key Takeaways -- Chapter 18: LSTM RNNs -- What Is LSTM -- The LSTM Cell -- Example -- LSTM with One Layer of 8 -- LSTM with Three Layers of 64 -- Conclusion -- Key Takeaways -- Chapter 19: The Prophet Model -- The Example -- The Prophet Data Format -- The Basic Prophet Model -- Adding Monthly Seasonality to Prophet -- Adding Holiday Data to Basic Prophet -- Adding an Extra Regressor to Prophet -- Tuning Hyperparameters Using Grid Search -- Key Takeaways -- Chapter 20: The DeepAR Model -- About DeepAR -- Model Training with DeepAR -- Predictions with DeepAR -- Probability Predictions with DeepAR -- Adding Extra Regressors to DeepAR -- Hyperparameters of the DeepAR -- Benchmark and Conclusion -- Key Takeaways -- Chapter 21: Model Selection -- Model Selection Based on Metrics -- Model Structure and Inputs -- One-Step Forecasts vs. Multistep Forecasts -- Model Complexity vs. Gain -- Model Complexity vs. Interpretability -- Model Stability and Variation -- Conclusion -- Key Takeaways -- Index. |
| Altri titoli varianti | Advanced Forecasting with Python |
| Record Nr. | UNINA-9910488729003321 |
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Korstanje Joos
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| Berkeley, CA : , : Apress L. P., , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Advanced Rust : Exploring Power Features of Rust for Intermediate and Advanced Developers
| Advanced Rust : Exploring Power Features of Rust for Intermediate and Advanced Developers |
| Autore | Azam Nouman |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Berkeley, CA : , : Apress L. P., , 2026 |
| Descrizione fisica | 1 online resource (204 pages) |
| Collana | Professional and Applied Computing Series |
| ISBN | 9798868829925 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9911127458803321 |
| Azam Nouman | ||
| Berkeley, CA : , : Apress L. P., , 2026 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Adversarial AI Threat Response and Secure Model Design : Practical Techniques for Detecting, Preventing, and Managing AI Vulnerabilities
| Adversarial AI Threat Response and Secure Model Design : Practical Techniques for Detecting, Preventing, and Managing AI Vulnerabilities |
| Autore | Trajkovski Goran |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Berkeley, CA : , : Apress L. P., , 2026 |
| Descrizione fisica | 1 online resource (363 pages) |
| Disciplina | 005.8 |
| Collana | Professional and Applied Computing Series |
| Soggetto topico | Artificial intelligence - Security measures |
| ISBN | 979-88-6882-308-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1: The AI Security Threat Field -- Chapter 2: Understanding Adversarial Examples -- Chapter 3: Attacks Beyond Vision -- Chapter 4: Advanced Threat Techniques -- Chapter 5: Detecting the Invisible -- Chapter 6: Building Robust Models -- Chapter 7: Defensive Preprocessing Techniques -- Chapter 8: Ensemble and Layered Defense Systems -- Chapter 9: Quantifying Adversarial Risk -- Chapter 10: Responsibility, Liability, and Law -- Chapter 11: Ethical Challenges and Disclosure -- Chapter 12: Societal Impact and Deepfakes -- Chapter 13: Emerging Threats -- Chapter 14: Tools and Libraries for Attack and Defense -- Chapter 15: Case Studies in Real-World Adversarial AI -- Chapter 16: Guided Hands-on Projects. |
| Record Nr. | UNINA-9911114338503321 |
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Trajkovski Goran
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| Berkeley, CA : , : Apress L. P., , 2026 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Agent Nation : How Autonomous AI Is Rewriting the Rules of Society--And What We Can Do about It
| Agent Nation : How Autonomous AI Is Rewriting the Rules of Society--And What We Can Do about It |
| Autore | Shah Chirag |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Berkeley, CA : , : Apress L. P., , 2026 |
| Descrizione fisica | 1 online resource (204 pages) |
| Disciplina | 006.3 |
| Collana | Professional and Applied Computing Series |
| Soggetto topico |
Artificial intelligence - Social aspects
Autonomic computing |
| ISBN | 9798868824548 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Agent Nation -- Preface -- Endorsements -- Acknowledgments -- Table of Contents -- About the Author -- 1. The Rise of Agent Nation -- 2. The New Digital Workforce -- 3. Algorithmic Governance -- 4. Intimate Algorithms -- 5. The Accountability Gap -- 6. Democratic AI Governance -- 7. Designing Human-Centered AI Agents -- 8. Reclaiming Human Agency -- References -- Index. |
| Record Nr. | UNINA-9911114488403321 |
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Shah Chirag
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| Berkeley, CA : , : Apress L. P., , 2026 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Agentic AI for Engineers : Architecting Goal-Driven Systems
| Agentic AI for Engineers : Architecting Goal-Driven Systems |
| Autore | Nagasubramanian Dhivya |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | 2026 |
| Descrizione fisica | 1 online resource (415 pages) |
| Disciplina | 006.3 |
| Collana | Professional and Applied Computing Series |
| Soggetto topico | Artificial intelligence - Engineering applications |
| ISBN | 979-88-6882-361-9 |
| Classificazione | COM004000COM051360MAT029000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9911113360403321 |
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Nagasubramanian Dhivya
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| 2026 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Agile Visualization with Pharo : Crafting Interactive Visual Support Using Roassal
| Agile Visualization with Pharo : Crafting Interactive Visual Support Using Roassal |
| Autore | Bergel Alexandre |
| Pubbl/distr/stampa | Berkeley, CA : , : Apress L. P., , 2021 |
| Descrizione fisica | 1 online resource (268 pages) |
| Soggetto genere / forma | Electronic books. |
| ISBN |
9781484271612
9781484271605 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Table of Contents -- About the Author -- About the Technical Reviewer -- Chapter 1: Introduction -- Agile Visualization -- The Pharo Programming Language -- The Roassal Visualization Engine -- Roassal License -- Contributing to the Development of Roassal -- Accompanying Source Code -- Want to Have a Chat? -- Book Overview -- Who Should Read This Book? -- Acknowledgments -- Chapter 2: Quick Start -- Installation -- First Visualization -- Visualizing the Filesystem -- Charting Data -- Sunburst -- Graph Rendering -- What Have You Learned in This Chapter? -- Chapter 3: Pharo in a Nutshell -- Hello World -- Visualizing Some Numbers -- From Scripts to Object-Oriented Programming -- Pillars of Object-Oriented Programming -- Sending Messages -- Creating Objects -- Creating Classes -- Creating Methods -- Block Closures -- Control Structures -- Collections -- Cascades -- A Bit of Metaprogramming -- What Have You Learned in This Chapter? -- Chapter 4: Agile Visualization -- Visualizing Classes as a Running Example -- Example in the Pharo Environment -- Closing Words -- What Have You Learned in This Chapter? -- Chapter 5: Overview of Roassal -- Architecture of Roassal -- Shapes -- Canvas -- Events -- Interaction -- Normalizer -- Layouts -- Inspector Integration -- Animation -- What Have You Learned in This Chapter? -- Chapter 6: The Roassal Canvas -- Opening, Resizing, and Closing a Canvas -- Camera and Shapes -- Virtual Space -- Shape Order -- Canvas Controller -- Converting a Canvas to a Shape -- Events -- What Have You Learned in This Chapter? -- Chapter 7: Shapes -- Box -- Circle and Ellipse -- Label -- Polygon -- SVG Path -- Common Features -- Model -- Line -- Line Attach Point -- Line Marker -- Line with Control Points -- What Have You Learned in This Chapter? -- Chapter 8: Line Builder -- Difficulties with Build Lines -- Using a Line Builder.
Using Associations -- Graph Visualization -- What Have You Learned in This Chapter? -- Chapter 9: Shape Composition -- Composite Shapes -- Model Object in Composite -- Labels Part of a Composition -- Labeled Circles -- What Have You Learned in This Chapter? -- Chapter 10: Normalizing and Scaling Values -- Normalizing Shape Size -- The RSNormalizer Class -- Combining Normalization -- Normalizing Shape Position -- Line Width -- Scaling -- What Have You Learned in This Chapter? -- Chapter 11: Interactions -- Useful Interactions -- Using Any Shape in a Popup -- RSLabeled -- RSHighlightable -- What Have You Learned in This Chapter? -- Chapter 12: Layouts -- Circle Layout -- Grid Layout -- Flow Layout -- Rectangle Pack Layout -- Line Layout -- Tree Layout -- Force-Based Layout -- Conditional Layout -- Graphviz Layouts -- Installing Graphviz -- Bridging Roassal and Graphviz -- Graphviz Layout -- What Have You Learned in This Chapter? -- Chapter 13: Integration in the Inspector -- Pharo Inspector -- Visualizing a Collection of Numbers -- Chaining Visualizations -- What Have You Learned in This Chapter? -- Chapter 14: Reinforcement Learning -- Implementation Overview -- Defining the Map -- Modeling State -- The Reinforcement Learning Algorithm -- Running the Algorithm -- What Have You Learned in This Chapter? -- Chapter 15: Generating Visualizations From GitHub -- Requirements -- Creating a Workflow -- Trying the Workflow -- Running Unit Tests -- Running Tests -- Visualizing the UML Class Diagram -- Visualizing the Test Coverage -- What Have You Learned in This Chapter? -- Index. |
| Record Nr. | UNINA-9910510544303321 |
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Bergel Alexandre
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| Berkeley, CA : , : Apress L. P., , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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AI Agents for Secure and Software-Defined Networking : Harnessing AI and SDN to Revolutionize Modern Work Environments
| AI Agents for Secure and Software-Defined Networking : Harnessing AI and SDN to Revolutionize Modern Work Environments |
| Autore | Mehta Het |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Berkeley, CA : , : Apress L. P., , 2026 |
| Descrizione fisica | 1 online resource (272 pages) |
| Collana | Professional and Applied Computing Series |
| ISBN | 9798868823589 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9911114495003321 |
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Mehta Het
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| Berkeley, CA : , : Apress L. P., , 2026 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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AI and Third-Party Risk : Solutions for Assessing and Managing Your AI Vendors and Systems
| AI and Third-Party Risk : Solutions for Assessing and Managing Your AI Vendors and Systems |
| Autore | Rasner Gregory C |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Berkeley, CA : , : Apress L. P., , 2026 |
| Descrizione fisica | 1 online resource (130 pages) |
| Disciplina | 005.8 |
| Collana | Professional and Applied Computing Series |
| Soggetto topico |
Computer security
Artificial intelligence |
| ISBN | 979-88-6882-465-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9911114324303321 |
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Rasner Gregory C
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| Berkeley, CA : , : Apress L. P., , 2026 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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AI Strategy and Security : A Roadmap for Secure, Responsible, and Resilient AI Adoption
| AI Strategy and Security : A Roadmap for Secure, Responsible, and Resilient AI Adoption |
| Autore | Wendt Donnie W |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Berkeley, CA : , : Apress L. P., , 2025 |
| Descrizione fisica | 1 online resource (195 pages) |
| Disciplina | 658.5/14 |
| Collana | Professional and Applied Computing Series |
| Soggetto topico |
Artificial intelligence - Business applications
Success in business Strategic planning |
| ISBN | 979-88-6881-733-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1, Strategy Development -- Chapter 2, Assess AI Readiness -- Chapter 3, The Team -- Chapter 4, Securing AI -- Chapter 5, Governance and Risks -- Chapter 6, Ensuring Responsible AI -- Chapter 7, Regulations and Standards -- Chapter 8, Operationalizing AI -- Chapter 9, Continuous Improvement -- Chapter 10, AI as a Way of Doing Business -- Chapter 11, The AI Adoption and Management Framework. |
| Record Nr. | UNINA-9911028762403321 |
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Wendt Donnie W
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| Berkeley, CA : , : Apress L. P., , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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