Synthetic Data and Generative AI
- 1st Edition - January 9, 2024
- Latest edition
- Author: Vincent Granville
- Language: English
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 synthe… Read more
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Description
Description
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.
Key features
Key features
- 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
Readership
Readership
Computer Scientists and researchers in Artificial Intelligence and Machine Learning, as well as practitioners in analytics in a variety of fields such as quant, engineering, statistics, operations research, biostatisticians, data scientists, data engineers, CTOs, and other decision makers. As such, academics, researchers, and professionals in a variety of research fields who work with AI, algorithms, big data, and machine learning and their applications to various real-world research and application problems will be a target audience. Upper-level undergrad and graduate students in Computer Science, AI, ML, applied mathematics, and data science.
Table of contents
Table of contents
1. Machine Learning Cloud Regression and Optimization
2. A Simple, Robust and Efficient Ensemble Method
3. Gentle Introduction to Linear Algebra – Synthetic Time Series
4. Image and Video Generation
5. Synthetic Clusters and Alternative to GMM
6. Shape Classification and Synthetization via Explainable AI
7. Synthetic Data, Interpretable Regression, and Submodels
8. From Interpolation to Fuzzy Regression
9. New Interpolation Methods for Synthetization and Prediction
10. Synthetic Tabular Data: Copulas vs enhanced GANs
11. High Quality Random Numbers for Data Synthetization
12. Some Unusual Random Walks
13. Divergent Optimization Algorithm and Synthetic Functions
14. Synthetic Terrain Generation and AI-generated Art
15. Synthetic Star Cluster Generation with Collision Graphs
16. Perturbed Lattice Point Process: Alternative to GMM
17. Synthetizing Multiplicative Functions in Number Theory
18. Text, Sound Generation and Other Topics
2. A Simple, Robust and Efficient Ensemble Method
3. Gentle Introduction to Linear Algebra – Synthetic Time Series
4. Image and Video Generation
5. Synthetic Clusters and Alternative to GMM
6. Shape Classification and Synthetization via Explainable AI
7. Synthetic Data, Interpretable Regression, and Submodels
8. From Interpolation to Fuzzy Regression
9. New Interpolation Methods for Synthetization and Prediction
10. Synthetic Tabular Data: Copulas vs enhanced GANs
11. High Quality Random Numbers for Data Synthetization
12. Some Unusual Random Walks
13. Divergent Optimization Algorithm and Synthetic Functions
14. Synthetic Terrain Generation and AI-generated Art
15. Synthetic Star Cluster Generation with Collision Graphs
16. Perturbed Lattice Point Process: Alternative to GMM
17. Synthetizing Multiplicative Functions in Number Theory
18. Text, Sound Generation and Other Topics
Product details
Product details
- Edition: 1
- Latest edition
- Published: January 12, 2024
- Language: English
About the author
About the author
VG
Vincent Granville
Dr. Vincent Granville is a pioneering data scientist and machine learning expert, co-founder of Data Science Central (acquired by TechTarget in 2020), founder of MLTechniques.com, former VC-funded executive, author, and patent owner. Dr. Granville’s past corporate experience includes Visa, Wells Fargo, eBay, NBC, Microsoft, and CNET. Dr. Granville is also a former post-doc at Cambridge University, and the National Institute of Statistical Sciences (NISS). Dr. Granville has published in Journal of Number Theory, Journal of the Royal Statistical Society, and IEEE Transactions on Pattern Analysis and Machine Intelligence, and he is the author of Developing Analytic Talent: Becoming a Data Scientist, Wiley. Dr. Granville lives in Washington state, and enjoys doing research on stochastic processes, dynamical systems, experimental math, and probabilistic number theory. He has been listed in the Forbes magazine Top 20 Big Data Influencers.
Affiliations and expertise
Author and Publisher, MLTechniques.com, USAView book on ScienceDirect
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