Supervised Learning with Neural Networks

Almost all the economic value created by neural networks has been through one type of machine learning, called supervised learning.

In supervised learning, you have some input x, and you want to learn a function mapping to some output y. So for example, just now we saw the housing price prediction application where you input some features of a home and try to output or estimate the price y.

Here are some examples of supervised learning

There are different types of neural network, for example Convolution Neural Network (CNN) used often for image application and Recurrent Neural Network (RNN) used for one-dimensional sequence data such as translating English to Chinses or a temporal component such as text transcript.

As for the autonomous driving, it is a hybrid neural network architecture.

You might also have heard about applications of machine learning to both Structured Data and Unstructured Data.

Structured Data means basically databases of data.

For example, in housing price prediction, you might have a database or the column that tells you the size and the number of bedrooms.

So that's structured data, meaning that each of the features, such as size of the house, the number of bedrooms, or the age of a user, has a very well defined meaning.

In contrast, unstructured data refers to things like audio, raw audio, or images where you might want to recognize what's in the image or text.

Here the features might be the pixel values in an image or the individual words in a piece of text.

Historically, it has been much harder for computers to make sense of unstructured data compared to structured data. And the fact the human race has evolved to be very good at understanding audio cues as well as images.

And then text was a more recent invention, but people are just really good at interpreting unstructured data.

And so one of the most exciting things about the rise of neural networks is that, thanks to deep learning, thanks to neural networks, computers are now much better at interpreting unstructured data as well compared to just a few years ago.

And this creates opportunities for many new exciting applications that use speech recognition, image recognition, natural language processing on text, much more than was possible even just two or three years ago.

So neural networks have transformed supervised learning and are creating tremendous economic value.