Restricted Boltzmann Machines -- Simplified – Towards Data Science

In this post, I will try to shed some light on the intuition about Restricted Boltzmann Machines and the way they work. This is supposed to be a simple explanation with a little bit of mathematics without going too deep into each concept or equation. So let's start with the origin of RBMs and delve deeper as we move forward. Boltzmann machines are stochastic and generative neural networks capable of learning internal representations and are able to represent and (given sufficient time) solve difficult combinatoric problems. They are named after the Boltzmann distribution (also known as Gibbs Distribution) which is an integral part of Statistical Mechanics and helps us to understand the impact of parameters like Entropy and Temperature on the Quantum States in Thermodynamics.