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

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