I'm Brian Bell, lead Software Engineer on Modeling Machine at DataRobot and I'm
here to talk about neural networks. First, I want to talk about the name. Why neural
nets? Well, early researchers were really inspired by some breakthroughs in brain
research. They took the idea of a neuron, which is a cell which transfers signals
to other neurons cells in the brain, as a building block for their research. They
thought if we want to create ML algorithms and AI, which could potentially
one day rival performance of the human brain,
we need to use this idea. So, it's probably a stretch to say that neural
networks operate anything like the human brain, but they are inspired by some
things that we've seen from the brain. They called their initial neurons
perceptrons. It's taking in several inputs, it applies a function, and then we
get an output. So the inputs could be reasons for a loan, or loan amount, or
credit score. It applies a function and then we get an output. In this case, the
output that we want is, will this default yes, or no. We need to learn how to apply
weights to this in such a way that we can figure out which of these is
important and in what quantities, and then combine all of that information
into a final answer. So in the old days, people were not that excited about
neural nets. Traditional models, modeled here as this sort of flattening out
curve, we're outperforming them. As we've been able to scale to more data and we
we actually can feasibly compute, neural nets eventually outperform traditional
models which tend to plateau actually. What's very unique about neural nets is
their ability to extend beyond the features that you've given it to detect
what we call deep patterns or subtle patterns or
underlying substructure in the input data.The second thing that I would say
about neural networks, apart from the fact that you can achieve fantastic
performance with large amounts of data, is that because of their structure and
their layered ness, many neurons passing signals to other neurons, they're able to
learn underlying structure or very subtle things that other models can't
pick up on.So in this case, information coming in from the left about this face
is broken up into individual inputs and then goes into each node. And then a
function is applied, waits, and then combined until we finally get to an
output. The network can learn something abstract such as, what is a curve or a
line or a dot, and then combine those into a curve and a dot means an eye, or a
dot and a line is a nose, or a curve and a dot is a mouth.This isn't strictly
speaking exactly how a neural network would actually look at a face, but what
you can say is that a neural network in the different layers (perhaps earlier
layers) is learning fundamental building blocks which are used in later layers to
more accurately determine what a face is than say a tree-based model. And that's
neural networks. A really powerful technique that scales to more data
and helps us achieve really cool performance on images on audio on all kinds of
things that techniques just can't really compete with.
