Code for Machine Learning for Trading, 3rd edition — from data sourcing to live execution.
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Mimesis is a Python library for generating fake but realistic data in multiple languages and locales.
Synthetic Patient Population Simulator
Python Toolkit for Causal and Probabilistic Reasoning
Database anonymization and test data management
Synthetic data generators for tabular and time-series data
The Declarative Data Generator
[IROS 2020] se(3)-TrackNet: Data-driven 6D Pose Tracking by Calibrating Image Residuals in Synthetic Domains
AgML is a centralized framework for agricultural machine learning. AgML provides access to public agricultural datasets for common agricultural deep learning tasks, with standard benchmarks and pretrained models, as well the ability to generate synthetic data and annotations.
This repository provides you with an easy-to-use labeling tool for State-of-the-art Deep Learning training purposes. It supports Auto-Labeling.
BENERATOR is a leading software solution to generate, obfuscate, pseudonymize and migrate data for development, testing, and training purposes with a model-driven approach.
Realistic synthetic events for testing, demos, and pipelines — streamed live or generated in bulk
Accurate segmentation of bacterial microscope images using deep learning synthetically generated image data.
Free sample: 42-table synthetic business dataset with double-entry accounting. AdventureWorks alternative. CSV + SQL + Parquet + SQLite. AU tax compliance (ATO/GST).