Synthetic Data and Generative AI

Synthetic Data and Generative AI

Synthetic Data and Generative AI covers the foundations of machine learning, with modern approaches to solving complex problems and the systematic generation and use of synthetic data. Emphasis is on scalability, automation, testing, optimizing, and interpretability (explainable AI). For instance, r...

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Detalles Bibliográficos
Otros Autores: Granville, Vincent, author (author)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Amsterdam, Netherlands : Elsevier [2024]
Edición:First edition
Materias:
Machine learning.
Artificial intelligence.
Computer vision.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009792869306719
Tabla de Contenidos:
  • Intro
  • Title page
  • Table of Contents
  • Copyright
  • Chapter 1: Machine learning cloud regression and optimization
  • Abstract
  • 1.1. Introduction: circle fitting
  • 1.2. Methodology, implementation details, and caveats
  • 1.3. Case studies
  • 1.4. Connection to synthetic data: meteorites, ocean tides
  • References
  • Chapter 2: A simple, robust, and efficient ensemble method
  • Abstract
  • 2.1. Introduction
  • 2.2. Methodology
  • 2.3. Implementation details
  • 2.4. Model-free confidence intervals and perfect nodes
  • References
  • Chapter 3: Gentle introduction to linear algebra - synthetic time series
  • Abstract
  • 3.1. Power of a matrix
  • 3.2. Examples, generalization, and matrix inversion
  • 3.3. Application to machine learning problems
  • 3.4. Mathematics of autoregressive time series
  • 3.5. Math for machine learning: must-read books
  • References
  • Chapter 4: Image and video generation
  • Abstract
  • 4.1. Introduction
  • 4.2. Applications
  • 4.3. Python code
  • 4.4. Visualizations
  • References
  • Chapter 5: Synthetic clusters and alternative to GMM
  • Abstract
  • 5.1. Introduction
  • 5.2. Generating the synthetic data
  • 5.3. Classification and unsupervised clustering
  • 5.4. Python code
  • References
  • Chapter 6: Shape classification and synthetization via explainable AI
  • Abstract
  • 6.1. Introduction
  • 6.2. Mathematical foundations
  • 6.3. Shape signature
  • 6.4. Shape comparison
  • 6.5. Application
  • 6.6. Exercises
  • References
  • Chapter 7: Synthetic data, interpretable regression, and submodels
  • Abstract
  • 7.1. Introduction
  • 7.2. Synthetic data sets and the spreadsheet
  • 7.3. Damping schedule and convergence acceleration
  • 7.4. Performance assessment on synthetic data
  • 7.5. Feature selection
  • 7.6. Conclusion
  • References
  • Chapter 8: From interpolation to fuzzy regression
  • Abstract
  • 8.1. Introduction.
  • 8.2. Original version
  • 8.3. Full, nonlinear model in higher dimensions
  • 8.4. Results
  • 8.5. Exercises
  • 8.6. Python source code and data sets
  • References
  • Chapter 9: New interpolation methods for synthetization and prediction
  • Abstract
  • 9.1. First method
  • 9.2. Second method
  • 9.3. Python code
  • References
  • Chapter 10: Synthetic tabular data: copulas vs enhanced GANs
  • Abstract
  • 10.1. Sensitivity analysis, bias reduction and other uses of synthetic data
  • 10.2. Using copulas to generate synthetic data
  • 10.3. Synthetization: GAN versus copulas
  • 10.4. Deep dive into generative adversarial networks (GAN)
  • 10.5. Comparing GANs with the copula method
  • 10.6. Data synthetization explained in one picture
  • 10.7. Python code: GAN to synthesize medical data
  • References
  • Chapter 11: High quality random numbers for data synthetization
  • Abstract
  • 11.1. Introduction
  • 11.2. Pseudorandom numbers
  • 11.3. Python code
  • 11.4. Military-grade PRNG based on quadratic irrationals
  • References
  • Chapter 12: Some unusual random walks
  • Abstract
  • 12.1. Symmetric unbiased constrained random walks
  • 12.2. Related stochastic processes
  • 12.3. Python code
  • References
  • Chapter 13: Divergent optimization algorithm and synthetic functions
  • Abstract
  • 13.1. Introduction
  • 13.2. Nonconverging fixed-point algorithm
  • 13.3. Generalization with synthetic random functions
  • 13.4. Smoothing highly chaotic curves
  • 13.5. Connection to synthetic data: random functions
  • References
  • Chapter 14: Synthetic terrain generation and AI-generated art
  • Abstract
  • 14.1. Introduction
  • 14.2. Terrain generation and the evolutionary process
  • 14.3. Python code
  • 14.4. AI-generated art with 3D contours
  • References
  • Chapter 15: Synthetic star cluster generation with collision graphs
  • Abstract
  • 15.1. Introduction.
  • 15.2. Model parameters and simulation results
  • 15.3. Analysis of star collisions and collision graph
  • 15.4. Animated data visualizations
  • 15.5. Python code and computational issues
  • Chapter 16: Perturbed lattice point process: alternative to GMM
  • Abstract
  • 16.1. Perturbed lattices: definition and properties
  • 16.2. Cluster processes and nearest neighbor graphs
  • 16.3. Statistical inference for point processes
  • 16.4. Special topics
  • References
  • Chapter 17: Synthetizing multiplicative functions in number theory
  • Abstract
  • 17.1. Introduction
  • 17.2. Euler products
  • 17.3. Finite Dirichlet series and generalizations
  • 17.4. Exercises
  • 17.5. Python code
  • References
  • Chapter 18: Text, sound generation, and other topics
  • Abstract
  • 18.1. Sound generation: let your data sing!
  • 18.2. Data videos and enhanced visualizations in R
  • 18.3. Dual confidence regions
  • 18.4. Fast feature selection based on predictive power
  • 18.5. NLP: taxonomy creation and text generation
  • 18.6. Automated detection of outliers and number of clusters
  • 18.7. Advice to beginners
  • References
  • Glossary
  • Index.

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