Systems engineering neural networks / / Alessandro Migliaccio, Giovanni Iannone

• Autore: Migliaccio Alessandro
• Pubblicazione: Hoboken, NJ : , : John Wiley & Sons, Inc., , [2023]; ©2023
• Descrizione fisica: 1 online resource (243 pages)
• Disciplina: 006.3/2
• Soggetto topico: Neural networks (Computer science); Computer simulation; Systems engineering
• Persona (resp. second.): IannoneGiovanni
• Nota di contenuto: Cover -- Title Page -- Copyright -- Contents -- About the Authors -- Acknowledgements -- How to Read this Book -- Part I Setting the Scene -- Chapter 1 A Brief Introduction -- 1.1 The Systems Engineering Approach to Artificial Intelligence (AI) -- 1.2 Chapter Summary -- Questions -- Chapter 2 Defining a Neural Network -- 2.1 Biological Networks -- 2.2 From Biology to Mathematics -- 2.3 We Came a Full Circle -- 2.4 The Model of McCulloch‐Pitts -- 2.5 The Artificial Neuron of Rosenblatt -- 2.6 Final Remarks -- 2.7 Chapter Summary -- Questions -- Sources -- Chapter 3 Engineering Neural Networks -- 3.1 A Brief Recap on Systems Engineering -- 3.2 The Keystone: SE4AI and AI4SE -- 3.3 Engineering Complexity -- 3.4 The Sport System -- 3.5 Engineering a Sports Club -- 3.6 Optimization -- 3.7 An Example of Decision Making -- 3.8 Futurism and Foresight -- 3.9 Qualitative to Quantitative -- 3.10 Fuzzy Thinking -- 3.11 It Is all in the Tools -- 3.12 Chapter Summary -- Questions -- Sources -- Part II Neural Networks in Action -- Chapter 4 Systems Thinking for Software Development -- 4.1 Programming Languages -- 4.2 One More Thing: Software Engineering -- 4.3 Chapter Summary -- Questions -- Source -- Chapter 5 Practice Makes Perfect -- 5.1 Example 1: Cosine Function -- 5.2 Example 2: Corrosion on a Metal Structure -- 5.3 Example 3: Defining Roles of Athletes -- 5.4 Example 4: Athlete's Performance -- 5.5 Example 5: Team Performance -- 5.5.1 A Human‐Defined‐System -- 5.5.2 Human Factors -- 5.5.3 The Sports Team as System of Interest -- 5.5.4 Impact of Human Error on Sports Team Performance -- 5.5.4.1 Dataset -- 5.5.4.2 Problem Statement -- 5.5.4.3 Feature Engineering and Extraction -- 5.5.4.4 Creation of Computed Columns -- 5.5.4.5 Explorative Data Analysis (EDA) -- 5.5.4.6 Extension ‐ Sampling Method for an Imbalanced Dataset. ; 5.5.4.7 Building a Neural Network Model -- 5.5.4.8 Training Outcome and Model Evaluation -- 5.5.4.9 Evaluate Using Test Data -- 5.6 Example 6: Trend Prediction -- 5.7 Example 7: Symplex and Game Theory -- 5.8 Example 8: Sorting Machine for Lego® Bricks -- 5.8.1 Challenge for Readers -- Part III Down to the Basics -- Chapter 6 Input/Output, Hidden Layer and Bias -- 6.1 Input/Output -- 6.2 Hidden Layer -- 6.2.1 How Many Hidden Nodes Should we Have? -- 6.3 Bias -- 6.4 Final Remarks -- 6.5 Chapter Summary -- Questions -- Source -- Chapter 7 Activation Function -- 7.1 Types of Activation Functions -- 7.2 Activation Function Derivatives -- 7.3 Activation Functions Response to W and b Variables -- 7.4 Final Remarks -- 7.5 Chapter Summary -- Questions -- Source -- Chapter 8 Cost Function, Back‐Propagation and Other Iterative Methods -- 8.1 What Is the Difference between Loss and Cost? -- 8.2 Training the Neural Network -- 8.3 Back‐Propagation (BP) -- 8.4 One More Thing: Gradient Method and Conjugate Gradient Method -- 8.5 One More Thing: Newton's Method -- 8.6 Chapter Summary -- Questions -- Sources -- Chapter 9 Conclusions and Future Developments -- Glossary and Insights -- Index -- EULA.
• Titolo autorizzato: Systems engineering neural networks
• ISBN: 1-119-90202-9; 1-119-90200-2
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910830792103321
• Lo trovi qui: Univ. Federico II