Continuing our tour through some of the 'top 100 awesome deep learning papers,' today we're turning our attention to the unsupervised learning and generative networks section. I've split the papers here into two groups. Today we'll be looking at: Building high-level features using large-scale unsupervised learning, Le et al., 2012 Generative Adversarial Nets, Goodfellow et … Continue reading Unsupervised learning and GANs

Today we're looking at the 'optimisation and training techniques' section from the 'top 100 awesome deep learning papers' list. Random search for hyper-parameter optimization, Bergstra & Bengio 2012 Improving neural networks by preventing co-adaptation of feature detectors, Hinton et al., 2012 Dropout: a simple way to prevent neural networks from overfitting, Srivastava et al., 2014 … Continue reading Optimisation and training techniques for deep learning

This is the first in a series of posts looking at the 'top 100 awesome deep learning papers.' Deviating from the normal one-paper-per-day format, I'll take the papers mostly in their groupings as found in the list (with some subdivision, plus a few extras thrown in) - thus we'll be looking at multiple papers each … Continue reading Understanding, generalisation, and transfer learning in deep neural networks

There have been several lists of deep learning papers doing the rounds. Recently Terry Taewoong Um's list of the top 100 awesome and most cited deep learning papers caught my eye. Deep learning is an exciting area and it's moving fast. I'd like to know what's in those 100 papers (thankfully, we have at least … Continue reading An experiment with awesome deep learning papers