[MUSIC] Okay, so I mentioned that this time, you only have the brain scans, but no classification problem in mind. So you just got the data. You don't have the particular cancer versus no cancer problem here. Actually, you have no labels at all. But what you want to do, you want to take the, wow, the multitude of your brain scans. We want to understood whether there is maybe some kind of structure to it. Maybe there's some kind of anomaly that some of the patients share. Or maybe, there's like one or several groups that share some explainable traits. It's kind of hard to do if you have 2D or 3D images that are poorly interpretable by humans. But there are special methods like, for example, Stochastic Neighborhood Embedding that can embed the larger dimensional data into the small dimensional size in order to kind of visualize it for you, so that you can plot the 2D or 3D scatter. Now, the only problem is that if you apply t-SNE into, if you apply it to, say, 100 by 100 pixel image, you could wait forever before it converges. And it's kind of not going to work, because basically, well, the [INAUDIBLE] complexity of t-SNE is just too large for this. Now, what you can do instead is you can use the autoencoder as a preliminary step. So you take the pixels and you can convert it into the hidden representation of where there's only maybe 100 or maybe 250 pixels on the slide hidden components that can then be embedded with t-SNE. Now, this is a much easier problem from the computational perspective. And you can use this to visualize even low-level data like images and sounds. Now, if this t-Stochastic Neighborhood Embedding doesn't sound familiar, it's one of the so-called many foldering methods. There is a lot of math and practical application behind it. But so far, let's just consider that they are one of the cool ways you can take your encoded 100 dimensional or 256 dimensional vectors. Map it into a super low dimensionality like a 2D point or a 3D point in a way that your original images, if they resemble one another, will be mapped to similar points, while different images will get more distant from one another. There is a neat picture on the slide, so I encourage you to pause the, well, presentation here and see if you can spot the symmetrical symantical coherence between similar images. But we'll cover more about the math of the embedding methods, the manifold learning methods later when we cover for example. So it more of less started out the compression and the feats representation finding property of unsupervised learning. Now, let's talk about generation data. So previously, we used the encoder only to generate features. Now, my construction in decoder is a part of the [INAUDIBLE] that was trained to make the errors transform and took the higher level features and it constructed an image that would correspond to those features. So it would take, for example, a particular description of a face and some high-level dense representation, and it would produce the face itself. What it allows you to do is say that you have two images where the first person has an, well, enormously large nose, for example. And the second one has an enormously small, okay, not enormously, tiny nose. Then you could find the latent vectors of those two images. And if you average the out, you have all chances to end up on an image that has an average-sized nose. This is a two example, but in general, this opens the domain of image morphing, so it is called. You can take your image in this hidden representation and find some vector that applies some, well, meaningful transformation to it, like adding smile or removing smile if it's a face. Or if it's a music, maybe adding some kind of, well, sound effect, or maybe even converting one instrument into the other. Now, here on the slide, you can see this trick applied to the 100 digits. It's the means dataset. You're probably familiar with it. So if a train on altering quarter on missed, you can expect the hidden representation of the altering quarter to correspond to image classes and handwriting styles. Well, it isn't remarkable, but it's quite a bit neural. Even without being shown that there is 10 labels, 10 classes, the models learn to map each hidden representation to a particular class label. So this can be done to discover the classification problem for you to then solve this supervised learning. The idea here is that if you have a decoder that was trained on such dataset, it was originally trained to convert this hidden representation back into the image. So if you take some hidden representation, then you can use it to generate an image. And if you alter it slightly, the image will be altered as well. So if you start, for example, from 7, it's the upper-left corner, and then you alter one feature by slight increments, you'll get the kind of incremental change from 7 to 1. And if you choose a different direction, it would be 7 to 0. It's a small example, but it kind of gives you the flavor of what can be done with alter. Now, when applied to image, it's the same kind of vector, the same incremental changes can lead you from a particular, say, face of a male person into a corresponding female person with all other features preserved to maximum possible state. Now the idea here is that you first want to understand whether such a direction exists. So you want to find out whether there is maybe a bunch of male people who have some some particular feature at a negative value and female people have a positive value. And if you take a male person and apply the female transformation to it you'll get a corresponding female person. Here on the image the same is applied for vector that kind of increases the age, so this is one way you can do this. Artificial intelligence predicts what's your future appearance going to look like at say 80. So there is a few neat demos using it, so here is a set of faces And the corresponding rules are present using editing quarters to add or remove smiles or eye glasses. Now there's of course more than one way you can train in your letter generic especially generic images. There's a special class of models called Generative Adversarial Networks and those are to train to generate And they kind of thought to be state of the art here. So they produce images that are slightly sharper, slightly more plausible. This so it is thought by the experts at this particular point of time because image generation is hard to judge. But the beauty of altering quarters is that they are able to solve so many tasks and being able to generate images is only an artifact here. They were not originally intended to do so. So basically you can think of as a very specific mathematical model which has a very specific loss function that grasps a very wide range of practical problems. We'll elaborate on those image generation methods and image morphing approaches later this week. [MUSIC]
