With data as the lifeblood of AI development, synthetic data is an elegantly simple concept to create on demand data needed for training and testing AI functions.
As a follow-up to Synthetic data generation with Isaac SIM tutorial, the engineer whom developed the Replicator synthetic data generation tool recently hosted a webinar. This is the same Replicator tool that we use to create datasets to train the DNN’s for ROS perception packages.
I had attended the Synthetic data generation training with Isaac. The monte carlo method was really helpful. I implemented it in developing a bot system that had real-data, because the chatbot development services could generate synthetic data by determining the best fit distributions for given real-data. If businesses want to fit real-data into a known distribution and they know the distribution parameters, businesses can they can use Monte Carlo method to generate synthetic data. I’m looking forward to this training as well