Self-Organizing maps (SOM)

Unsupervised Neural Network

An unsupervised neural network is a type of artificial neural network (ANN) used in unsupervised learning tasks. Unlike supervised neural networks, trained on labeled data with explicit input-output pairs, unsupervised neural networks are trained on unlabeled data. In unsupervised learning, the network is not under the guidance of features. Instead, it is provided with unlabeled data sets (containing only the input data) and left to discover the patterns in the data and build a new model from it. Here, it has to figure out how to arrange the data by exploiting the separation between clusters within it. These neural networks aim to discover patterns, structures, or representations within the data without specific guidance....

Autoencoder

A neural network that learn how to compress and decompress data. They are trained to reconstruct the input data from a compressed representation, which is further learned by the network during training of the model. It can be used for tasks such as image compression, dimensionality reduction, and denoising. Autoencoders are feedforward neural networks with an encoder and a decoder. They aim to map input data to a lower-dimensional representation and then reconstruct the data....

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....

Self-Organizing maps (SOM)

Kohonen maps, or self-organizing maps (SOM), are an intriguing class of unsupervised neural network models. They work especially well for comprehending and interpreting complex, high-dimensional data. The capacity of SOMs to reduce dimensionality while maintaining the topological linkages in the input data is one of its distinguishing features. A SOM starts with a grid of neurons, each of which represents a particular area of the data space. Through a process of competitive learning, the neurons adjust to the distribution of the input data throughout training. Neighboring neurons change their weights in response to similar data points, making them more sensitive to these patterns. This self-organizing characteristic produces a map that places comparable data points in close proximity to one another, making it possible to see patterns and clusters in the data....

Generative Adversarial Networks (GANs)

Generative Adversarial Networks (GANs) are a novel paradigm in the field of unsupervised neural networks. The discriminator and generator neural networks that make up a GAN are always in conflict with one another. As the discriminator works to separate authentic from produced data, the generator aims to create data samples that are identical to real data. After training, the generator in a GAN learns to produce data that is more and more realistic, starting out as random noise. Image generation, style transfer, and data augmentation all benefit from the adversarial training process that pushes the generator to provide data that is frequently very convincing. Drug discovery and natural language processing are two more fields in which GANs are being used....

Implementation of Restricted Boltzmann Machine

Let’s dive deep into the implementation of unsupervised neural network models Restricted Boltzmann machine that is given by the Sklearn or Scikit-learn...

Advantages of Unsupervised Neural network models

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Disadvantages of Unsupervised Neural network models

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