 Dear Fellow Scholars, this is two-minute papers with Károly Zsolnai-Fehér, to all of you Fellow Scholars out there who are yearning for some more deep learning action like I do. Here goes the second package. Buckle up amazing applications await you. As always, links to every one of these works are available in the description box. This convolutional neural network can learn how to colorize by looking at the same images both in color and black and white. The first image is the black and white input. The second is how the algorithm colorized it, and the third is how the image originally looked like in color. Insanity. Recurrent neural networks are able to learn and produce sequences of data, and they are getting better and better at music generation. Nowadays, people are experimenting with human-added music generation with pretty amazing results. Sony has also been working on such a solution with spectacular results. One can also run a network on a large database of leaked human passwords and try to crack new accounts building on that knowledge. Deep neural networks take a substantial amount of time to train, and the final contents of each of the neurons have to be stored, which takes a lot of space. New techniques are being explored to compress the information content of these networks. There's another application where endangered whale species are recognized by convolutional neural networks. Some of them have a worldwide population of less than 500, and this is where machine learning steps in to try to save them. Awesome. YouTube has a huge database full of information on what kind of video thumbnails are the ones that people end up clicking on. They use deep learning to automatically find and suggest the most appealing images for your videos. There is also this crazy application where a network was trained on a huge dataset with images of celebrities. A low-quality image is given where the algorithm creates a higher resolution version building on this knowledge. The leftmost images are the true high resolution images, the second one is the grainy, low resolution input, and the third is the neural network's attempt to reconstruct the original. This application takes your handwriting of a number and visualizes how a convolutional neural network understands and classifies it. Apparently, George R.R. Martin is late with writing the next book of Game of Thrones, but luckily we have recurrent neural networks that can generate text in his style. An infinite amount, so beware George, winter is coming. I mean, the machines are coming. It is truly amazing what these techniques are capable of, and as machine learning is a remarkably fast moving field, new applications pop up pretty much every day. I am quite enthused to do at least one more batch of these. Of course, provided that you liked this one. Let me know. Thanks for watching and for your generous support, and I'll see you next time.