The key idea behind the probabilistic framework to machine learning is that learning can be thought of as inferring plausible models to explain observed data. A machine can use such models to make ...

There are times to design with parametric features and times to work with free-form versions. A few advantages of designing in 3D solids include associative detail drawings, creating complex features, ...