Restricted Boltzmann Machine
A probabilistic model serves as the foundation for Restricted Boltzmann Machines (RBMs), which are unsupervised nonlinear feature learners. A linear classifier, such as a linear SVM or a perceptron, can often yield strong results when given features extracted by an RBM or a hierarchy of RBMs. About how the inputs are distributed, the model makes assumptions. Only BernoulliRBM is currently available through scikit-learn, and it expects that the inputs are binary or between 0 and 1, each of which encodes the likelihood that a given feature will be enabled. The RBM uses a specific graphical model to attempt to optimize the likelihood of the data. The representations capture interesting regularities since the parameter learning approach (Stochastic Maximum Likelihood) keeps them from deviating from the input data. However, this makes the model less useful for small datasets and typically not useful for density estimation.
Each RBM has an energy function that measures the compatibility between visible and hidden unit states. The energy of an RBM is defined as follows:
where E(v,h) is the energy of RBM of given state, Wij is weight, and ai and bj are biases.
The joint probability of a visible unit configuration (v) and a hidden unit configuration (h) is calculated using the energy function:
and for the marginal probabilities for visible and hidden units are obtained by summing over the respective variables:
Unsupervised Neural Network Models
Unsupervised learning is an intriguing area of machine learning that reveals hidden structures and patterns in data without requiring labelled samples. Because it investigates the underlying relationships in data, it’s an effective tool for tasks like anomaly identification, dimensionality reduction, and clustering. There are several uses for unsupervised learning in domains like computer vision, natural language processing, and data analysis. Through self-sufficient data interpretation, it provides insightful information that enhances decision-making and facilitates comprehension of intricate data patterns.
There are many types of unsupervised learning, but here in this article, we will be focusing on Unsupervised neural network models.
Table of Content
- Unsupervised Neural Network
- Autoencoder
- Restricted Boltzmann Machine
- Self-Organizing maps (SOM)
- Generative Adversarial Networks (GANs)
- Implementation of Restricted Boltzmann Machine
- Advantages of Unsupervised Neural network models
- Disadvantages of Unsupervised Neural network models
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