Sabrina Polin: Training a computer to learn like a
human brain -- that's deep learning. Sometimes
referred to as deep neural networking or deep
neural learning, it's associated with artificial
intelligence, and through deep learning computers,
"learn" to recognize patterns and identify
abstract objects. To understand deep learning,
imagine a toddler learning about dogs. Let's say
the toddler learns about dogs by pointing to
objects, with the parents replying, "Yes, that is
a dog," or "No, that is not a dog." As the toddler
continues to point to objects, he or she becomes
more aware of dog features, like a tail and ears,
and fur and four paws. The toddler is clarifying a
complex abstraction -- the concept of a dog -- by
building a hierarchy of which each level of
abstraction is created with knowledge that was
gained by the preceding layer of the hierarchy. If
you've ever seen a program that can recognize a
flower species based on a photo, or song based on
the sound of someone humming it, that is a result
of a neural network. Beyond image and song
recognitions, deep learning applications can be
found in speech recognition and translation
software, and even self driving cars. Deep
learning has its limitations, however. Deep
learning models learn through observation, and
they only know what they're trained on. A deep
learning model trained on a small or irrelevant
data set will learn in ways that aren't ultimately
useful to the task at hand.