– you really need to start using neural networks. When the patterns get really complex,

neural nets start to outperform all of their competition. Plus, GPUs can train them faster

than ever before! Let's take a look.

Neural nets truly have the potential to revolutionize the field of Artificial Intelligence. We all

know that computers are very good with repetitive calculations and detailed instructions, but

they've historically been bad at recognizing patterns. Thanks to deep learning, this is

all about to change.

If you only need to analyze simple patterns, a basic classification tool like an SVM or

Logistic Regression is typically good enough. But when your data has 10s of different inputs

or more, neural nets start to win out over the other methods. Still, as the patterns

get even more complex, neural networks with a small number of layers can become unusable.

The reason is that the number of nodes required in each layer grows exponentially with the

number of possible patterns in the data. Eventually training becomes way too expensive and the

accuracy starts to suffer. So for an intricate pattern – like an image of a human face,

for example – basic classification engines and shallow neural nets simply aren’t good

enough – the only practical choice is a deep net.

Have you ever run into a wall when trying to work with highly complex data? Please comment

and let me know your thoughts.

But what enables a deep net to recognize these complex patterns? The key is that deep nets

are able to break the complex patterns down into a series of simpler patterns. For example,

let's say that a net had to decide whether or not an image contained a human face. A

deep net would first use edges to detect different parts of the face – the lips, nose, eyes,

ears, and so on – and would then combine the results together to form the whole face.

This important feature – using simpler patterns as building blocks to detect complex patterns

– is what gives deep nets their strength. The accuracy of these nets has become very

impressive – in fact, a deep net from google recently beat a human at a pattern recognition

challenge.

It’s not surprising that deep nets were inspired by the structure of our own human

brains. Even in the early days of neural networks, researches wanted to link a large number of

perceptrons together in a layered web – an idea which helped improve their accuracy.

It is believed that our brains have a very deep architecture and that we decipher patterns

just like a deep net – we detect complex patterns by first detecting, and combining,

the simple ones.

There is one downside to all of this – deep nets take much longer to train. The good news

is that recent advances in computing have really reduced the amount of time it takes

to properly train a net. High performance GPUs can finish training a complex net in

under a week, when fast CPUs may have taken weeks or even months.

Before we talk more about the various Deep Learning models, we're going to briefly

discuss which types of deep nets are suitable for different machine learning tasks. That's

coming up in the next video.