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There are a lot of different kinds of neural networks, and some of them are just fine, given how effective they are, but it turns out that almost all of the economic value created by neural networks so far is essentially dependent on a machine learning category called supervised learning. In supervised learning you have some input XXX, and you want to learn a function to map to some output YYY, such as housing price prediction example, you just input some characteristics about the house, and try to output or estimate the price YYy.
One of the most profitable areas for deep learning today is online advertising. It may not be the most inspiring, but it’s profitable. By putting information about an AD on the site, and because you’re putting information about the user, the site decides whether or not to show you the AD.
Neural networks have gotten really good at predicting whether you’re going to click on that AD, by showing users the ads they’re most likely to click on, and that’s one way in which neural networks have been incredibly profitable for many companies. The ability to show you the ads you’re most likely to click on has a direct impact on the revenue of some of the largest online advertising companies.
Computer vision has also made great strides in the past few years, thanks to deep learning. You can put in an image and you want to output an index, ranging from 1 to 1000 to try to tell you about the photo, and it could be any one of, say, 1,000 different images, so you might choose to tag the photo with it.
The recent advances in deep learning in speech recognition are also very exciting. You can now feed audio clips into a neural network and have it output a transcript. Machine translation has also made great strides thanks to deep learning. You can use a neural network to input an English sentence and then output a Chinese sentence.
In autonomous driving, you can input an image, like an information radar showing what’s in front of the car, and from that you can train a neural network to tell the car exactly where it is on the road, which is a key component of the autonomous driving system.
Perhaps for real estate and online advertising might be relatively standard neural networks. For image applications, we often use Convolutional Neural Network, often abbreviated to CNN, on Neural networks. For sequential data, such as audio, there is a time component where the audio is played out over time, so audio is the most natural representation. As a one-dimensional time series (two Kinds of English words temporal sequence). For serial data, RNN, a Recurrent Neural Network, is often used. Languages, English and Chinese alphabres or words occur one by one, so languages are also the most natural serial data, so more complex RNNs versions are often used in these applications.