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Table of contents

Key takeawaysCollaboration platforms are essential to the new way of workingEmployees prefer engati over emailEmployees play a growing part in software purchasing decisionsThe future of work is collaborativeMethodologyA Boltzmann machine is an unsupervised deep learning model in which every node is connected to every other node. It is a type of recurrent neural network, and the nodes make binary decisions with some level of bias.

These machines are not deterministic deep learning models, they are stochastic or generative deep learning models. They are representations of a system.

A Boltzmann machine has two kinds of nodes

- Visible nodes:

These are nodes that can be measured and are measured. - Hidden nodes:

These are nodes that cannot be measured or are not measured.

According to some experts, a Boltzmann machine can be called a stochastic Hopfield network which has hidden units. It has a network of units with an ‘energy’ defined for the overall network.

Boltzmann machines seek to reach thermal equilibrium. It essentially looks to optimize global distribution of energy. But the temperature and energy of the system are relative to laws of thermodynamics and are not literal.

They use stochastic binary units to reach probability distribution equilibrium (to minimize energy).

The machine is named after Ludwig Boltzmann, an Austrian scientist who came up with the Boltzmann distribution. However, this type of network was first developed by Geoff Hinton, a Stanford Scientist.

The Boltzmann distribution is a probability distribution that gives the probability of a system being in a certain state as a function of that state's energy and the temperature of the system.

It was formulated by Ludwig Boltzmann in 1868 and is also known as the Gibbs distribution.

There are three types of Boltzmann machines. These are:

- Restricted Boltzmann Machines (RBMs)
- Deep Belief Networks (DBNs)
- Deep Boltzmann Machines (DBMs)

While in a full Boltzmann machine all the nodes are connected to each other and the connections grow exponentially, an RBM has certain restrictions with respect to node connections.

In a Restricted Boltzmann Machine, hidden nodes cannot be connected to each other while visible nodes are connected to each other.

In a Deep Belief Network, you could say that multiple Restricted Boltzmann Machines are stacked, such that the outputs of the first RBM are the inputs of the subsequent RBM. The connections within individual layers are undirected, while the connections between layers are directed. However, there is an exception here. The connection between the top two layers is undirected.

A deep belief network can either be trained using a Greedy Layer-wise Training Algorithm or a Wake-Sleep Algorithm.

Deep Boltzmann Machines are very similar to Deep Belief Networks. The difference between these two types of Boltzmann machines is that while connections between layers in DBNs are directed, in DBMs, the connections within layers, as well as the connections between the layers, are all undirected.

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