I did some nn stuff years ago (before the days of ReLU) then came back to it in the last year or two.
After reading many papers and catching up with the ideas, I'm doing some simple little things to learn Python and the interacies of Torch.
The first thing I made was a tiny upscaler to go from 16x16 rgb to 32x32 rgb. Next was an autoencoder to turn a 32x32 rgb into a number of bytes (128 in testing) and back.
Next up is a combination of both to autoencode correction data to correct an upscale.
It's been well worth it for learning the programming interface to the ideas. Wrangling training data has also been valuable experience for something that isn't terribly complex but is easier once you've done it a few times.
After reading many papers and catching up with the ideas, I'm doing some simple little things to learn Python and the interacies of Torch.
The first thing I made was a tiny upscaler to go from 16x16 rgb to 32x32 rgb. Next was an autoencoder to turn a 32x32 rgb into a number of bytes (128 in testing) and back.
Next up is a combination of both to autoencode correction data to correct an upscale.
It's been well worth it for learning the programming interface to the ideas. Wrangling training data has also been valuable experience for something that isn't terribly complex but is easier once you've done it a few times.