Restricted Boltzmann machines

• In the early 90s, neural networks had largely gone out of fashion. The bulk of machine learning research was around other techniques, such as random forests and support vector machines. Neural networks with only a single hidden layer were less performant than these other techniques, and it was thought that deeper neural networks were too difficult to train.
• The resurgence of interest in neural networks was spearheaded by Geoffrey Hinton, who, in 2004, led a team of researchers who proceeded to make a series of breakthroughs using restricted Boltzmann machines (RBM) and creating neural networks with many layers; they called this approach deep learning. Within 10 years, deep learning would go from being a niche technique to dominating every single AI competition. RBMs were part of the big breakthrough, allowing Hinton and others to get world record scores on a variety of image and speech recognition problems.
• In this section, we will look at the theory of how...