About this document
Simulated Annealing in Boltzmann Machines by Gopal Garg is a document available to read on EtoBox.
Simulated annealing is an optimization technique used to train neural networks like the Boltzmann machine. [1] The Boltzmann machine uses probabilistic weight updates and simulated annealing, gradually lowering a temperature parameter T, to find the global minimum during training and avoid local minima. [2] Learning occurs in two phases: first weights between co-active units are incremented, then weights between co-active input and hidden units are decremented to "unlearn" poor associations. [3] However, Bo
- Author
- Gopal Garg
- Language
- EN