Understanding Models Developed by AI: Including Applications with Python and MATLAB Code is a comprehensive guide on the intricacies of AI models and their real-world applications. The book demystifies complex AI methodologies by providing clear explanations and practical examples that are reinforced with Python and MATLAB program codes. Its content structure emphasizes a practical, applications-driven approach to understanding AI models, with hands-on coding examples throughout each chapter. Readers will find the tools they need to build AI models, along with the knowledge to make these models accessible and interpretable to stakeholders, thus fostering trust and reliability in AI systems.
As the primary issues with the adoption of AI/ML models are reliability, transparency, interpretation of results, and bias (data and algorithm) management, this resource give researchers and developers what they need to be able to not only implement AI models, but also interpret and explain them. This is crucial in industries where decision-making processes must be transparent and understandable.
1. 1 Introduction - p. 215 2. 2 Materials and methods - p. 217 3. 3 Results and Discussion - p. 226 4. 3.1 Dataset 1 (proximate) - p. 226 5. 3.2 Dataset 2 (ultimate) - p. 231 6. 3.3 Dataset 3 (proximate & ultimate) - p. 231 7. 3.4 Dataset 4 (Density & viscosity & iodine value & saponification value) - p. 238 8. 3.5 Dataset 5 - p. 242 9. 3.5.1 DashtiS1 (proximate) - p. 242 10. 3.5.2 Ghugare (proximate) - p. 245 11. 3.5.3 NhuchhenS2 (proximate & ultimate) - p. 247 12. 3.5.4 Proximate - p. 250 13. 3.5.5 Qian 2016 (proximate & ultimate) - p. 253 14. 3.6 Dataset 6 (ultimate) - p. 257 15. 3.7 Dataset 7 (proximate) - p. 260 16. 4 Conclusion - p. 262 17. Chapter 10. Interpreting deep learning models: Exploring advanced deep learning techniques with a focus on interpretability - p. 267 18. Chapter 11. AI ethics and responsible use: Ethical considerations in AI, focusing on the implications of model interpretability - p. 287 19. Index - p. 303
Understanding Models Developed by AI: Including Applications with Python and MATLAB Code is a comprehensive guide on the intricacies of AI models and their real-world applications. The book demystifies complex AI methodologies by providing clear explanations and practical examples that are reinforced with Python and MATLAB program codes. Its content structure emphasizes a practical, applications-driven approach to understanding AI models, with hands-on coding examples throughout each chapter. Readers will find the tools they need to build AI models, along with the knowledge to make these models accessible and interpretable to stakeholders, thus fostering trust and reliability in AI systems.
As the primary issues with the adoption of AI/ML models are reliability, transparency, interpretation of results, and bias (data and algorithm) management, this resource give researchers and developers what they need to be able to not only implement AI models, but also interpret and explain them. This is crucial in industries where decision-making processes must be transparent and understandable.
1. 1 Introduction - p. 215 2. 2 Materials and methods - p. 217 3. 3 Results and Discussion - p. 226 4. 3.1 Dataset 1 (proximate) - p. 226 5. 3.2 Dataset 2 (ultimate) - p. 231 6. 3.3 Dataset 3 (proximate & ultimate) - p. 231 7. 3.4 Dataset 4 (Density & viscosity & iodine value & saponification value) - p. 238 8. 3.5 Dataset 5 - p. 242 9. 3.5.1 DashtiS1 (proximate) - p. 242 10. 3.5.2 Ghugare (proximate) - p. 245 11. 3.5.3 NhuchhenS2 (proximate & ultimate) - p. 247 12. 3.5.4 Proximate - p. 250 13. 3.5.5 Qian 2016 (proximate & ultimate) - p. 253 14. 3.6 Dataset 6 (ultimate) - p. 257 15. 3.7 Dataset 7 (proximate) - p. 260 16. 4 Conclusion - p. 262 17. Chapter 10. Interpreting deep learning models: Exploring advanced deep learning techniques with a focus on interpretability - p. 267 18. Chapter 11. AI ethics and responsible use: Ethical considerations in AI, focusing on the implications of model interpretability - p. 287 19. Index - p. 303
