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.
There are several components of unsupervised learning. They are:
- Encoder-Decoder: As the name itself suggests that it is used to encode and decode the data. Encoder basically responsible for transforming the input data into lower dimensional representation on which the neural network works. Whereas decoder takes the encoded representation and reconstruct the input data from it. There architecture and parameters are learned during the training of the network.
- Latent Space: It is the immediate representation created by the encoder. It contains the abstract representation or features that captures important information about the data’s structures. It is also known as the latent space.
- Training algorithm: Unsupervised neural network model use specific training algorithms to get the parameters. Some of the common optimization algorithms are Stochastic gradient descent, Adam etc. They are used depending on the type of model and loss function.
- Loss Function: It is a common component among all the machine learning models. It basically calculates the model’s output and the actual/measured output. It quantifies how well the model understands the data.
Now, Let’s discuss about some of the types of unsupervised neural network.
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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