Neural Network learns the Mandelbrot set [Part 1]

Every frame is rendered by passing each pixel's x, y coordinates through a neural network, which outputs a value between (0, 1) that is used as the pixel's value. This network is trained to approximate the value of the Mandelbrot function evaluated at x, y, so the image improves as the network learns. The color does not indicate anything about the approximation, I just think it looks cool. See the code for more details: https://github.com/MaxRobinsonTheGreat/mandelbrotnn This is an overfit problem, or rather, there is no such thing as "over" fitting. You can only ever come short of an accurate fit. Ultimately it would require larger networks, longer training times, ever diminishing learning rates, and larger samples of training data points to improve the approximation, but it is interesting to see just how well the network can fit the set with pragmatic limitations. The final attempt took about 10 hours to train on my poor MX150, so I don't think I'll be scaling up anytime soon. Timestamps: Music: https://soundcloud.com/user-82623269