In 2006–2011, “deep learning” was popular, but “deep learning” mostly meant stacking unsupervised learning algorithms on top of each other in order to define complicated features for ...

The deep learning model uses this to create a feature set for a cat and builds a predictive model based on this. Initially, it might classify everything with four legs and a tail as a cat.

With deep learning, the program doesn’t start out with pre-fed information. Instead, it uses an algorithm to determine how many lines the shapes have, if those lines are connected, and if they ...

Engineering diagram analysis has emerged as a pivotal discipline within the modern engineering landscape. Deep learning techniques are increasingly utilised to automatically digitise, interpret ...

He also points out that deep learning requires enormous amounts of data for training. Just because you have the algorithm, he says, that doesn’t mean you’ll get useful results.

Today’s deep learning algorithms are simply too primitive to encode the complex subjective and semantic decision-making processes that underlie many tasks without the deep manual domain ...

Buzzwords like “deep learning” and “neural networks” are everywhere, but so much of the popular understanding is misguided, says Terrence Sejnowski, a computational neuroscientist at the ...