adam_carrigan's comments

Code: "AICONF-HN"

Send us an email - Adam at MindsDB.com and Jorge at MindsDB.com

Thanks, it has been amazing working with you also.

Did you have one in mind? We add integrations based on community demand.

The design is modular such that it can support anything under the cover.

Essentially you have encoders for all of the columns, which then get piped into a mixer and then into decoders to predict the final output(s). These encoders and decoders can be any type of ML model, but our current focus is on neural networks.

So e.g. if you have say a text like "A cute cat" and the number 5 and your target is an image (let's assume you have a training set such that the model would learn to generate one with 5 cute cats) then you have:

1. Text encoder generates an embedding for (cute cat) + numerical encoder normalizes "5" 2. A mixer (which can be e.g. an FCNN or gradient booster) generates an intermediate representation. 3. A decoder that is trained to generate images takes that representation and generates an image1.

Note: above is a good illustrative example, in practice, we're good with outputting dates, numerical, categories, tags and time-series (i.e. predicting 20 steps ahead). We haven't put much work into image/text/audio/video outputs

You should be able to find more details about how we do this in the docs and most of the heavy lifting happens in the lightwood repo, the code for that is fairly readable I hope: https://github.com/mindsdb/lightwood