In this webinar, we explore questions like, What is data augmentation, how does it work, and why is it so much harder to augment text than images?

Machine learning tasks are low-level building blocks that can only solve complex, real-world problems after being unified across type/task.

How can Jaxon train models to produce data that trains better models? Isn’t the first better if it can label data? Which is first, the egg or chicken?

Building ML pipelines is like solving a puzzle, but the right tools make the process easier, faster, and with a more stable end result.

After you use Jaxon to label your training set and are ready to embark on training classifiers, it might seem that you’re in the home stretch. However, if you want to obtain…

Get better, faster results using fewer resources with machine learning amplification tools like Jaxon. Hop on the fast track to gold!

We had hubris a couple years ago, thinking you could create accurate machine learning (ML) models completely unsupervised. Turns out, we do need some human supervision—just enough human knowledge to train the model properly.

Building machine learning models can be exhilarating, but the process to get to the final model can often be tedious. Inexperienced data science teams may encounter hidden obstacles they either can't handle or don't know about.

Confusion matrices are the tried-and-true way to evaluate the metrics for machine learning classifiers. Is updating them really so confusing?

Long story short, humans are slow, taking at least 10 seconds to label one example. However, a machine can make the same judgement in milliseconds.