Explanation of deep learning

Let's take a demand forecast example. Consider that you manage a website where t-shirts are sold. And you want to know how many units and t-shirts you anticipate selling dependent on how you price the t-shirts. Then you could make a dataset similar to this one, with demand decreasing as the price of the t-shirt increases. Therefore, you might fit a straight line to this data to demonstrate that demand decreases as price increases.

 

This is maybe the simplest possible neural network with a single artificial neuron, that just inputs the price and outputs the estimated demand. 

If you think of this orange circle, this artificial neuron as a little Lego brick, all that a neural network is; if you take a lot of these Lego bricks and stack them on top of each other until you get a big power, a big network of these neurons. Let's look at a more complex example. Suppose that instead of knowing only the price of the t-shirts, you also have the shipping costs that the customers will have to pay to get the t-shirts. Maybe you spend more or less on marketing in a given week, and you can also make the t-shirt out of a thick, heavy, expensive cotton or a much cheaper, more lightweight material. These are some of the factors that you think will affect the demand for your t-shirts. Let's see what a more complex neural network might look like. You know that your consumers care a lot about affordability. So let's say you have one neuron, and let me draw this one in blue, whose job it is to estimate the affordability of the t-shirts. And so affordability is mainly a function of the price of the shirts and of the shipping cost.

 A second thing though affecting demand for your t-shirts is awareness. How much are consumers aware that you're selling this t-shirt?

 So the main thing that affects awareness, is going to be your marketing. So let me draw here a second artificial neuron that inputs your marketing budget, how much you spend on marketing, and outputs how aware are consumers of your t-shirt. Finally, the perceived quality of your product will also affect demand, and perceived quality would be affected by marketing. The marketing tries to convince people this is a high-quality t-shirt, and sometimes the price of something also affects perceived quality. So I'm going to draw here a third artificial neuron that inputs price, marketing and material, and tries to estimate the perceived quality of your t-shirts. Finally, now that the earlier neurons, these three blue neurons, have figured out how affordable, how much consumer awareness and what's the perceived quality, you can then have one more neuron over here that takes as input these three factors and outputs the estimated demand. So this is a neural network, and its job is to learn to map from these four inputs, that's the input A, to the output B, to demand. So it learns this input-output or A to B mapping. This is a fairly small neural network with just four artificial neurons. In practice, neural networks used today are much larger, with easily thousands, tens of thousands, or even much larger than that numbers of neurons.

Now, there's just one final detail of this description that I want to clean up, which is that in the way I've described the neural network, it was as if you had to figure out that the key factors are affordability, awareness and perceived quality. One of the wonderful things about using neural networks is that to train a neural network, in other words, to build a machine learning system using neural network, all you have to do is give it the input A and the output B. And it figures out all of the things in the middle by itself. So to build a neural network, what you would do is feed it lots of data, or the input A, and have a neural network that just looks like this, with a few blue neurons feeding to a yellow output neuron. And then you have to give it data with the demand B as well. And it's the software's job to figure out what these blue neurons should be computing, so that it can completely automatically learn the most accurate possible function mapping from the input A to the output B. And it turns out that if you give this enough data and train a neural network that is big enough, this can do an incredible good job mapping from inputs A to outputs B. So that's a neural network, is a group of artificial neurons each of which computes a relatively simple function. But when you stack enough of them together like Lego bricks, they can compute incredibly complicated functions that give you very accurate mappings from the input A to the output B. Now, in this video, you saw an example of neural networks applied to demand prediction. Let's go on to the next video to see a more complex example of neural networks applied to face recognition.