- 【博客 & 代码】Generative Adversarial Networks (GANs) in 50 lines of code (PyTorch)
In 2014, Ian Goodfellow and his colleagues at the University of Montreal published a stunning paper introducing the world to GANs, or generative adversarial networks. Through an innovative combination of computational graphs and game theory they showed that, given enough modeling power, two models fighting against each other would be able to co-train through plain old backpropagation.
The models play two distinct (literally, adversarial) roles. Given some real data set R, G is the generator, trying to create fake data that looks just like the genuine data, while D is the discriminator, getting data from either the real set or G and labeling the difference. Goodfellow’s metaphor (and a fine one it is) was that G was like a team of forgers trying to match real paintings with their output, while D was the team of detectives trying to tell the difference. (Except that in this case, the forgers G never get to see the original data — only the judgments of D. They’re like blind forgers.)
2.【博客】Deep Learning Trends @ ICLR 2016
Started by the youngest members of the Deep Learning Mafia , namely Yann LeCun and Yoshua Bengio, the ICLR conference is quickly becoming a strong contender for the single most important venue in the Deep Learning space. More intimate than NIPS and less benchmark-driven than CVPR, the world of ICLR is arXiv-based and moves fast.
3.【课程】Deep Learning for Self-Driving Cars : Lecture 5
It doesn’t matter if you are beginner or new to machine learning or advanced researcher in the field of deep learning methods and their application, everybody can benefit of Lex Fridman’s course on Deep Learning for Self-Driving Cars.
If you are interested in this course, you can go to http://selfdrivingcars.mit.edu/ and Register an account on the site to stay up-to-date. The material for the course is free and open to the public.
In order to reach more people who are interested in Deep learning, machine learning and artificial intelligence, I would like to share this course material on my website.
This lecture introduces “Deep Learning for Human-Centered Semi-Autonomous Vehicles”.
4.【博客】Generating color palettes
As a designer one of the first things I do when starting a new project is to get a sense of the color palette. Making color palettes is a difficult process because while most people can tell when a combination of colors is pleasing, it's hard to explain exactly why. It's thus doubly difficult to produce something that's pleasing and fits with certain pre-requisites, like branding guidelines.
In fact this is something I still have trouble with, and more often than not I resort to a "guess and check" approach, sampling colors from online color generators and sometimes photography. This is obviously pretty tedious and I've long thought that there should be a way to automate the process, somehow distilling the required intuition into a machine learning model.
5.【博客】LETS LEARN ABOUT XOR ENCRYPTION
One of the more common things about this generation is the constant desire to write up (type) their thoughts. So many of the conversations from my high school days were long lasting, but quickly forgotten. Today’s generation is much more likely to blog, tweet, write status updates or simply open up a notepad file and write up their thoughts after such a conversation.