Recent content by freyabuilde

A useful way to understand Machine Learning is to separate the goal, the input, the model or tool, and the way the output is evaluated. One important area is quality versus speed. Define what a good result looks like before changing tools or settings, then test one variable at a time so you can...

I think productivity and way is the key part of this topic. A practical approach is to define the outcome you want, test it with a small example, and then check quality, speed and reliability separately. It also gives you a cleaner baseline if you want to compare tools or settings later.

A practical understanding of Boston Dynamics starts with knowing what the system does well, where it is unreliable, and how to validate the result. One important area is creative uses. Define what a good result looks like before changing tools or settings, then test one variable at a time so...