Synthetic Data and Generative AI / Vincent Granville.

Format:

Book

Author/Creator:

Granville, Vincent, author.

Language:

English

Subjects (All):

Machine learning.

Artificial intelligence.

Computer vision.

Physical Description:

1 online resource (410 pages)

Edition:

First edition.

Place of Publication:

Amsterdam, Netherlands : Elsevier, [2024]

Summary:

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, regression techniques - including logistic and Lasso - are presented as a single method, without using advanced linear algebra. Confidence regions and prediction intervals are built using parametric bootstrap, without statistical models or probability distributions. Models (including generative models and mixtures) are mostly used to create rich synthetic data to test and benchmark various methods. Emphasizes numerical stability and performance of algorithms (computational complexity) Focuses on explainable AI/interpretable machine learning, with heavy use of synthetic data and generative models, a new trend in the field Includes new, easier construction of confidence regions, without statistics, a simple alternative to the powerful, well-known XGBoost technique Covers automation of data cleaning, favoring easier solutions when possible Includes chapters dedicated fully to synthetic data applications: fractal-like terrain generation with the diamond-square algorithm, and synthetic star clusters evolving over time and bound by gravity.

Contents:

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

2.1. Introduction

2.2. Methodology

2.3. Implementation details

2.4. Model-free confidence intervals and perfect nodes

Chapter 3: Gentle introduction to linear algebra - synthetic time series

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

Chapter 4: Image and video generation

4.1. Introduction

4.2. Applications

4.3. Python code

4.4. Visualizations

Chapter 5: Synthetic clusters and alternative to GMM

5.1. Introduction

5.2. Generating the synthetic data

5.3. Classification and unsupervised clustering

5.4. Python code

Chapter 6: Shape classification and synthetization via explainable AI

6.1. Introduction

6.2. Mathematical foundations

6.3. Shape signature

6.4. Shape comparison

6.5. Application

6.6. Exercises

Chapter 7: Synthetic data, interpretable regression, and submodels

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

Chapter 8: From interpolation to fuzzy regression

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

Chapter 9: New interpolation methods for synthetization and prediction

9.1. First method

9.2. Second method

9.3. Python code

Chapter 10: Synthetic tabular data: copulas vs enhanced GANs

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

Chapter 11: High quality random numbers for data synthetization

11.1. Introduction

11.2. Pseudorandom numbers

11.3. Python code

11.4. Military-grade PRNG based on quadratic irrationals

Chapter 12: Some unusual random walks

12.1. Symmetric unbiased constrained random walks

12.2. Related stochastic processes

12.3. Python code

Chapter 13: Divergent optimization algorithm and synthetic functions

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

Chapter 14: Synthetic terrain generation and AI-generated art

14.1. Introduction

14.2. Terrain generation and the evolutionary process

14.3. Python code

14.4. AI-generated art with 3D contours

Chapter 15: Synthetic star cluster generation with collision graphs

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

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

Chapter 17: Synthetizing multiplicative functions in number theory

17.1. Introduction

17.2. Euler products

17.3. Finite Dirichlet series and generalizations

17.4. Exercises

17.5. Python code

Chapter 18: Text, sound generation, and other topics

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

Glossary

Index.

Notes:

Includes bibliographical references and index.

Description based on print version record.

ISBN:

9780443218569

0443218560

OCLC:

1417109232