more than NUMBER_TOKEN minutes where oh what's i'm using up so much of my time just to put this in the page here okay okay so there's my NUMBER_TOKEN minute timer i don't want to i want to be starting on the new stuff by the time that hits zero okay um so quickly i am very interested in machine learning generative machine learning models models that generate synthetic images text perhaps sound other kinds of media uh if you have been following the world of deep learning today the what you probably have heard of is something like oh again or stylegan or stylegandNUMBER_TOKEN or stylegandNUMBER_TOKEN and then there's this like latent space oh all these images are in the latent space and i can walk through the latent space and look at this ai is dreaming about cats in a style gantu layton space of all cats cats and many more cats if you have no idea what any of that means how could you possibly get started to learn some of the vocabulary feel comfortable with working with these systems even if you're ultimately just an end user of pre-trained models you're not designing and training the models yourself for me the process of me trying to sort out how all this stuff works and understand be able to read the style gand paper and maybe understand a bit more about it begins with auto encoders an auto encoder is a very simple um well i don't shouldn't say very simple because none of this is particularly simple but it is a good starting point to think about how generative models work and uh the last live stream i went through building this whole diagram to talk about how we could build something called a copying machine out of a neural network so if an image is sent into the neural network if we could just get that same image back out then somehow the neural network would have learned some type of like represent internal representation of that image in a way that it can reproduce it so obviously to copy an image is a simple well this is a simple process with an algorithm i can just say for every pixel make a new image take an existing image for every pixel put the new pixel in the new image so the auto encoder is not an efficient copying machine but it does give us this ability to copy the image while also compressing the data because the idea is if we start if i have a NUMBER_TOKEN by NUMBER_TOKEN image with NUMBER_TOKEN pixels as the data moves through these layers of the neural network we have fewer like i have NUMBER_TOKEN inputs the first layer might only have half of that in neurons and then another layer might have half of that and then so these numbers have to somehow be compressed into a smaller amount of numbers that then get expanded back out to the original image so this is like a copying machine and an image compression engine it is not an efficient image compression engine because if we really want to do image compression to save high resolution image with less file space on a on our hard drive we could just use jpeg or other kind of known tried and true image compression algorithms but if we are able to take the these images compress them down and have the neural network the auto code to learn how to uh decode them right we have an encoder and a decoder then at some point and hopefully i'll get to this part today i can take off after i've trained it i can take out the encoder and just start with
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