Links - March 31st, 2016
I have spent a lot of time lately trying to understand, and playing around with, computer vision, deep learning, reinforcement learning, and other machine learning models. Between Stanford’s Convolutional Neural Networks for Visual Recognition course, Trask’s neural stack explanation, playing snake with Keras and creating image analogies, I have spent a lot more time than usual coding and training machine learning models after work. More accurately, I have spent hours poring over complex equations I don’t yet fully understand, and waiting for models to converge.
Even if you are not a computer scientist, you should read the intro to reinforcement learning linked below. With some understanding of economic modeling, and a bit of effort, you’ll get the basics of how a system like AlphaGo works. Machine, or otherwise, learning is fun.