I am working with Keras and experimenting with AI and Machine Learning. I have a few projects made already and now I'm looking to replicate a dataset. What direction do I go to learn this? What should I be looking up to begin learning about this model? I just need an expert to point me in the right direction.
To clarify; by replicating a dataset I mean I want to take a series of numbers with an easily distinguishable pattern and then have the AI generate new data that is similar.
There are several ways to generate new data similar to a current dataset, but the most prominent way nowadays is to use a Generative Adversarial Network (GAN). This works by pitting two models against one another. The generator model attempts to generate data, and the discriminator model attempts to tell the difference between real data and generated data. There are plenty of tutorials out there on how to do this, though most of them are probably based on image data.
If you want to generate labels as well, make a conditional GAN.
The only other common method for generating data is a Variational Autoencoder (VAE), but the generated data tend to be lower-quality than what a GAN can generate. I don't know if that holds true for non-image data, though.