The neural network discussed in this post, called the Boltzmann machine, is a stochastic and recurrent network. This post contains my exam notes for the course TDT4270 Statistical image analysis and learning and explains the network’s properties, activation and learning algorithm.
In this essay, written as my final essay in the course IT3708 spring 2010, I discuss two phenomenas that are the driving forces for a lot of the complex biological systems in nature with several simple components that interact with each other. These two phenomenas are competition and cooperation. I will discuss examples from the process of evolution, where competition play a crucial part, to ant colonies, where cooperation between ants enables these simple creatures to do extraordinary things. All of the examples presented here have an inspiration from biology and has been applied successfully to computer AI problems. I start by defining competitive and cooperative interactions among individual components in a broad sense. Then six examples, with competitive and cooperative interactions, are presented. I finished off with a conclusion on the advantages and disadvantages of using these two phenomenas in AI systems.
This neural network proposed by Hopfield in 1982 can be seen as a network with associative memory and can be used for different pattern recognition problems. This post contains my exam notes for the course TDT4270 Statistical image analysis and learning and explains the network properties, activation and learning algorithm of the Hopfield network.