Related Question: Learning weights in a Boltzmann Machine
I'm trying to understand RBMs and how they are applied in training of Deep Architecture. Being new to the field of statistics, I stumbled upon various notations.
Question 1: What does this symbol represent: <$s_is_j$>
It is said while updating weights of RBMs that, the binary states of each node in hidden unit is set to 1 with a probability p equal to a function (a sigmoid function with the input: the sum of product of visible vector and corresponding weights vector connecting a hidden node).
Question 2: Why is every hidden unit being set to 1 (with probability p) and what is the function of the probability p?
Question 3: As far as I understand weights are updated once the system goes to equilibrium, so when is the system said to be in equilibrium?
Also is there any step-by-step procedures for training a network using RBMs