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Synthetic data generator
Synthetic data generator







  1. #Synthetic data generator how to#
  2. #Synthetic data generator code#

#Synthetic data generator how to#

Finally, you will learn how to produce high quality, impactful data animations (data videos). You will also learn the mathematics, methodology, and principles behind the scene. This book will teach you how to design rich, useful synthetic data to apply in various contexts. It also constitutes a solid introduction to scientific computing. Synthetic data can then be used to supplement, augment and.

#Synthetic data generator code#

It unifies separate, independent pieces of code for the first time. The Synthetic Data Vault (SDV) is a Synthetic Data Generation ecosystem of libraries that allows users to easily learn single-table, multi-table and timeseries datasets to later on generate new Synthetic Data that has the same format and statistical properties as the original dataset. The code is also on GitHub, spreading across multiple top-level folders. When needed, modern or new statistical learning techniques are introduced: dual confidence regions, new test of independence, parametric bootstrap, Rayleigh test, distribution-free logistic regression, proxy estimation and minimum contrast estimators, as well as a new prime test for strong pseudo-random number generators.Ībout 15% of the content is Python code with documentation. The author introduces a simple alternative to XGBoost, one of the most efficient ensemble methods it is applied to an NLP problem - categorizing and ranking articles and blog posts to predict future performance. Topics cover computer vision, natural language processing, tabular data, time series, geospatial and sound data, supervised classification, clustering, generative models, nearest neighbors and collision graphs, data-driven inference, prediction (all regression techniques fit into a single, easy-to-understand method), deep neural networks, modeling without response (unsupervised regression such as circle or curve fitting), constrained optimization, and more. Terrain generation, evolution and morphing (video frame, chapter 11) What You Will Learn From The Book Chapter 14 on the Riemann Hypothesis illustrates this point, with new state-of-the-art research results on the subject. Conversely mathematics benefits from these techniques to uncover new insights on the most famous math problems. The latter is an infinite source of synthetic data to build and benchmark new machine learning techniques. The author also added more recent advances with applications to terrain generation (with animated data), synthetic universes and experimental math. Not only it integrates all the material from his previous book “Intuitive Machine Learning and explainable AI”, but it also contains all but the most advanced math from his book on stochastic simulations. This book is the culmination of years of research on the topic, by the author. Synthetic data also contributes to eliminating algorithm biases and privacy issues. Finally, since synthetic data is not directly linked to real people or transactions, it offers protection against data leakage. It also helps with unbalanced data, for instance in fraud detection.

synthetic data generator

Thus, it contributes to the development of explainable AI. In addition, it helps understand decisions from obscure systems such as deep neural networks.

synthetic data generator synthetic data generator

It enriches them and allow black-box systems to correctly classify observations or predict values that are well outside of training and validation sets. Synthetic data is used more and more to augment real-life datasets.









Synthetic data generator