One of the biggest mental sinkholes into which AI students can get trapped is not quite understanding the fundamental difference between how our two basic “building block” networks operate: the Multilayer Perceptron (MLP), trained with backpropagation (or any form of gradient descent learning), and the (restricted) Boltzmann machine (RBM), trained with contrastive divergence. It’s easy… Continue reading When a Classifier Acts as an Autoencoder, and an Autoencoder Acts as a Classifier (Part 1 of 3)
Tag: RBMs
How Backpropagation and (Restricted) Boltzmann Machine Learning Combine in Deep Architectures
One of the most important things for us to understand, as we come into the “deep learning” aspect of AI (for the first time), is the relationship between backpropagation and the (restricted) Boltzmann machines, which we know comprise the essential core of various “deep learning” architectures. The essential idea in deep architectures is this: Each… Continue reading How Backpropagation and (Restricted) Boltzmann Machine Learning Combine in Deep Architectures